The Effect of Fermented Milk Containing Lactobacillus Casei Strain Shirota on Sarcopenia in Elderly Taiwanese: Interactions With the Nutrients Utilization, Diversity of Gut Microbiota, Microbiota-derived Metabolites and Muscle Loss
Phase: NA
Conditions: Sarcopenia, Inflammation, Aging
Sponsor: Taipei Medical University

 
 
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Title : The Effect of Fermented Milk Containing 
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Lactobacillus casei Strain Shirota on Sarcopenia in 
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Elderly Taiwanese: Interactions with the Nutrients 
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Utilization, 
Diversity 
of 
Gut 
Microbiota, 
4 
Microbiota-derived Metabolites and Muscle loss 
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NCT Number : NCT04985877 
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IRB number : N202010025 
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Principal Investigator :  Hui-Yu Huang  
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Co-Principal Investigator: Chieh-Hsi Wu 
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Co-Investigator: Tsan-Hon Liou 
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Co-Investigator: Shy-Shin Chang 
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Co-Investigator: Kai-Wei Liao  
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Co-Investigator: Cheng-Ching Lin  
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Co-Investigator: Yung-Chun Chang 
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Contents 
35 
1. 
Introduction ............................................................................................................ 8 
36 
1.1 
Possible Mechanisms of Sarcopenia ...................................................... 8 
37 
1.2 
Aging Gut Microbiota ............................................................................ 8 
38 
1.3 
Microbiota-derived Metabolites ............................................................. 9 
39 
1.4 
Trace elements ....................................................................................... 9 
40 
1.5 
Regulatory T cells ................................................................................ 10 
41 
1.6 
Beneficial Effects of Lactobacillus casei strain Shirota (LcS) on GI 
42 
Function, Brain Function, and Inflammation ........................................................... 10 
43 
1.7 
Preliminary Work ................................................................................. 10 
44 
2. 
Study Objective .................................................................................................... 11 
45 
2.1 
Primary objective ................................................................................. 11 
46 
2.2 
Secondary objective ............................................................................. 11 
47 
3. 
Study plan ............................................................................................................. 12 
48 
3.1 
Overall designs ..................................................................................... 12 
49 
3.2 
Efficacy assessment ............................................................................. 12 
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3.3 
Primary and Secondary Endpoints ....................................................... 12 
51 
4. 
Study intervention ................................................................................................. 13 
52 
4.1 
Study supplementation and administration .......................................... 13 
53 
4.2 
Restriction and withdrawal .................................................................. 13 
54 
5. 
Subject Enrollment ............................................................................................... 14 
55 
5.1 
Inclusion Criteria ................................................................................. 14 
56 
5.2 
Exclusion Criteria ................................................................................ 14 
57 
5.3 
Algorithm of recruitment and grouping ............................................... 15 
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5.3.1 
Step 1: First Screened cases ..................................................................... 15 
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5.3.2 
Step 2: Second screened and confirm sarcopenia .................................... 15 
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5.3.3 
Workflow of participant’s recruitment and grouping .............................. 17 
61 
6. 
Material and method ............................................................................................. 18 
62 
6.1 
Demographic data and medical history ................................................ 18 
63 
6.2 
Anthropometric measurement and Body Composition Assessment .... 18 
64 
6.2.1 
Bioelectrical impedance analysis (BIA) .................................................. 18 
65 
6.3 
Muscle strength .................................................................................... 18 
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6.4 
Physical performance ........................................................................... 18 
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6.4.1 
Walk speed ............................................................................................... 18 
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6.4.2 
TUG ......................................................................................................... 19 
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6.4.3 
SPPB ........................................................................................................ 19 
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6.5 
Gut microbiota composition and microbiota-derived metabolite 
71 
analysis   .………………………………………………………………………..19 
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6.5.1 
Fecal sample collection and bacterial genomic DNA isolation ............... 19 
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6.5.2 
16S rRNA amplicon and data analysis .................................................... 19 
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6.5.3 
Fecal Short Chain Fatty Acids (SCFAs), IPA and TMAO Determination
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…………………………………………………………………………..20 
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6.6 
Analysis of blood biochemical parameters .......................................... 20 
77 
6.6.1 
Whole blood, serum and plasma samples preparation ............................. 20 
78 
6.6.2 
Biochemical parameters ........................................................................... 20 
79 
6.6.3 
Analysis of pro-inflammatory and anti-inflammatory cytokines ............. 21 
80 
6.6.4 
Analysis of antioxidant enzymes ............................................................. 22 
81 
6.6.5 
Determination of blood metal elements ................................................... 22 
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6.6.6 
Analysis of anabolic hormone ................................................................. 22 
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6.6.7 
Analysis of immune function ................................................................... 22 
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6.7 
Questionnaire ....................................................................................... 22 
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6.7.1 
Basic information ..................................................................................... 22 
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6.7.2 
24-Hour dietary recall .............................................................................. 23 
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6.7.3 
International physical activity questionnaire (iPAQ) .............................. 23 
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6.7.4 
Food frequency questionnaire .................................................................. 23 
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6.7.5 
Taiwan lifestyle assessment ..................................................................... 23 
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6.7.6 
Patient Assessment of Constipation symptom (PAC-SYM) ................... 23 
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6.7.7 
SARC-F questionnaire ............................................................................. 23 
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6.7.8 
Mini nutrition assessment (MNA) ........................................................... 23 
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6.8 
Statistical analysis ................................................................................ 23 
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7. 
Results .................................................................................................................. 25 
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7.1 
Sarcopenia vs healthy subjects: Participants evaluation ...................... 25 
96 
7.1.1 
Basic characteristics ................................................................................. 25 
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7.1.2 
Anthropometric measurement .................................................................. 26 
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7.1.3 
Physical performance ............................................................................... 26 
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7.1.4 Dietary Assessment (MNA, 24h-recall, and Food frequency questionnaire)
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…………………………………………………………………………..27 
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7.1.5 
Patient Assessment of Constipation symptom ......................................... 27 
102 
7.1.6 
Blood biochemical parameters ................................................................. 28 
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7.1.7 
T cell subtypes ......................................................................................... 29 
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7.1.8 
Pro-inflammatory and anti-inflammatory cytokines ................................ 29 
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7.1.9 
Antioxidant activity ................................................................................. 29 
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7.1.10 Anabolic hormone .................................................................................... 30 
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7.1.11 Metal concentration ................................................................................. 30 
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7.1.12 Gut microbiota ........................................................................................... 30 
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7.2 
Influence of Fermented Milk Containing Lactobacillus casei Strain 
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Shirota intervention in Sarcopenia elderly ............................................................... 31 
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7.2.1 
Anthropometric measurement .................................................................. 31 
112 
7.2.2 
Physical performance ............................................................................... 31 
113 
7.2.3 
Dietary Assessment (MNA, 24h-recall) .................................................. 32 
114 
7.2.4 
Constipation symptom assessment .......................................................... 33 
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7.2.5 
Blood biochemical parameters ................................................................. 33 
116 
7.2.6 
T cell subtypes ......................................................................................... 34 
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7.2.7 
Pro-inflammatory and anti-inflammatory cytokines ................................ 34 
118 
7.2.8 
Antioxidant activity ................................................................................. 35 
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7.2.9 
Anabolic hormone .................................................................................... 35 
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7.2.10 Metal concentration ................................................................................. 35 
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7.2.11 Gut microbiota composition and microbiota-derived Metabolite Analysis
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…………………………………………………………………………..36 
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7.3 
Spearman correlations between gut microbiota and specific sarcopenia 
124 
parameter 
………………………………………………………………………..37 
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8. 
Discussion ............................................................................................................. 39 
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Overview and Primary Endpoint ................................................................................. 39 
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References .................................................................................................................... 47 
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Tables 
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Table 1. Nutrition fact of Yakult 300 LIGHT Fermented Milk ................................... 13 
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Table 2. Normal value of biochemical parameters ...................................................... 21 
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Table 3. Baseline characteristics for the PP population ............................................... 52 
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Table 4. Descriptive statistics of basic information on demographics of subjects. ..... 53 
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Table 5. Descriptive statistics of lifestyle on demographic characteristics of subjects
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 ....................................................................................................................... 54 
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Table 6. Comparison of body composition between the NS and SC groups ............... 56 
139 
Table 7. Comparison of physical performance between the NS and SC groups ......... 57 
140 
Table 8. Comparison of physical activity by IPAQ between NS and SC groups ........ 57 
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Table 9. Comparison of nutritional assessment by MNA score between NS and SC 
142 
groups............................................................................................................. 58 
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Table 10. Comparison of nutritional assessment by 24 hour-recall methods between 
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NS and SC groups .......................................................................................... 58 
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Table 11. Descriptive statistics of Food frequency ingestion on demographic 
146 
characteristics of subjects .............................................................................. 59 
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Table 12. Comparison severity of constipation symptoms by PAC-SYM between the 
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NS and SC groups .......................................................................................... 60 
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Table 13. Comparison of biochemical parameters between NS and SC groups ......... 61 
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Table 14. Comparison of different T lymphocyte types between the NS and SC groups.
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 ....................................................................................................................... 62 
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Table 15. Comparison of Pro-inflammatory and anti-inflammatory cytokines between 
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NS and SC groups .......................................................................................... 63 
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Table 16. Comparison of antioxidant activity between NS and SC groups ................ 64 
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Table 17. Comparison of anabolic hormone between NS and SC groups ................... 65 
156 
Table 18. Comparison of variety metals level in the serum between NS and SC groups
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 ....................................................................................................................... 66 
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Table 19. Differentially Abundant Genera Between NS and SC Identified by LEfSe 
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and MetagenomeSeq ...................................................................................... 72 
160 
Table 20 Comparison of the results of anthropometric measurements between pre- 
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and post-intervention in the SC and LcS groups using bioelectrical 
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impedance analysis (BIA). ............................................................................. 73 
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Table 21. Comparison of physical performance between pre- and post-intervention in 
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the SC and LcS groups................................................................................... 75 
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Table 22. Comparison of pre- and post-intervention physical activity using IPAQ 
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between SC and LcS groups .......................................................................... 76 
167 
Table 23. Comparison of physical activity pre- and post-intervention using MNA 
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between SC and LcS groups. ......................................................................... 77 
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Table 24. Comparison of nutritional assessment using 24-hour recall methods pre- and 
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post-intervention, and between SC and LcS groups. ..................................... 77 
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Table 25. Comparison of constipation symptom severity using PAC-SYM for pre- and 
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post-intervention, and between SC and LcS groups. ..................................... 78 
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Table 26. Comparison of biochemical parameters Pre and Post intervention in the SC 
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group. ............................................................................................................. 79 
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Table 27. Comparison of biochemical parameters Pre and Post intervention in LcS 
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group .............................................................................................................. 80 
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Table 28. Comparison of biochemical parameters between post-tests of the SC and 
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LcS groups ..................................................................................................... 81 
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Table 29. Comparison of T lymphocyte subsets pre- and post-intervention, and 
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between SC and LcS groups using flow cytometry. ...................................... 82 
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Table 30. Comparison of pro- and anti-inflammatory cytokines pre- and 
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post-intervention, and between SC and LcS groups using ELISA. ............... 83 
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Table 31. Comparison of antioxidant enzyme activity pre- and post-intervention, and 
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between SC and LcS groups using the ELISA method. ................................ 84 
185 
Table 32. Comparison of anabolic hormone levels pre- and post-intervention, and 
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between SC and LcS groups using the ELISA method. ................................ 85 
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Table 33. Comparison of metal concentrations pre- and post-intervention, and 
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between SC and LcS groups using ICP-MS. ................................................. 86 
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Table 34. Differentially Abundant Genera Between LcS and SC Identified by LEfSe 
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and MetagenomeSeq ...................................................................................... 93 
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Table 35 . Comparison of metabolite analysis among NS, SC, and LcS groups using 
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LC-MS/MS. ................................................................................................... 94 
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Figures 
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Figure 1. Workflow of participant’s recruitment and grouping ................................... 17 
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Figure 2. Participant flowchart of patients eligible, recruited, numbers followed up 
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and included in analysis. ................................................................................ 51 
199 
Figure 3. Alterations of gut microbiota composition between control group (NS) and 
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Sarcopenia Control (SC). ............................................................................... 69 
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Figure 4. MetagenomeSeq analysis of gut microbiota compared NS and SC group. .. 71 
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Figure 5. Alterations of gut microbiota composition between Sarcopenia Control (SC) 
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and Sarcopenia + Lacticaseibacillus casei Shirota (LcS) groups. ................. 89 
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Figure 6. MetagenomeSeq analysis of gut microbiota compared SC group and LcS 
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group. ............................................................................................................. 91 
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Figure 7. The spearman correlations between gut microbiota and physical 
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examinations .................................................................................................. 95 
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Figure 8. the Spearman correlations between gut microbiota and body compositions 96 
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Figure 9. The Spearman correlations between gut microbiota and cytokines, DHEA-S 
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and CD4 lymphocyte ..................................................................................... 97 
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Figure 10. The Spearman correlations between gut microbiota and biochemical 
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parameters ...................................................................................................... 98 
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Figure 11. The Spearman correlations between gut microbiota and heavy metals...... 99 
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Figure 12. The Spearman correlations between gut microbiota and metabolites ...... 100 
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1. Introduction 
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Aging-associated diseases represent a growing issue in modern society due to the 
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ever-increasing proportion of elderly individuals. Sarcopenia refers to the progressive 
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loss of skeletal muscle mass and strength and shares many characteristics with other 
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disease states typically associated with risks of falling and fracture, including 
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osteoporosis, frailty, and obesity. It is a common geriatric syndrome; its prevalence 
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has been estimated to be up to 35% in hospital wards [1, 2] . Osteoporosis, frailty, and 
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obesity result in negative health outcomes later in life [3]. Therefore, investigating 
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sarcopenia is an important component of ensuring ‘healthy aging’.  
225 
1.1 Possible Mechanisms of Sarcopenia 
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Many hypothesis have been suggested to explain sarcopenia: an increase in 
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connective tissue, muscle steatosis, impaired muscle metabolism, an increase in 
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inflammatory markers (e.g., TNF-α and IL-6), an increased stiffness of myofibers, 
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slower kinetics in establishing myosin-actin crossing bridges, increased oxidative 
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stress, mitochondria dysfunction, hormonal imbalance, and decreased capillary flow 
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[4-8]. The production of reactive oxygen and nitrogen species (RONS) and oxidative 
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damage has long been thought to be a potential mechanism of age-related muscle 
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atrophy via the radical theory of aging. According to this theory, RONS damage 
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proteins, lipids, and DNA, leading to tissue dysfunction [9] . Moreover, an essential 
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role has been proposed for RONS in regulating the IGF-AKT-mTOR signaling 
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pathway [10] , which, in turn, can directly impact control of muscle protein turnover. 
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The contribution of RONS to age-related muscle decline remains debatable, and the 
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use of antioxidant therapies has proven to be largely unsuccessful [11]. Antioxidants 
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potentially have adverse effects on muscle [12]. As such, while their role is difficult to 
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define in humans there is little evidence that redox imbalances are key drivers of 
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either age-related muscle atrophy or human disuse atrophy [13]. 
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1.2 Aging Gut Microbiota  
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Gut microbiota constitute a large, diverse, and dynamic ecosystem that generates 
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cross talk between the gut and host. It can communicate with the host by modulating 
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the gut-brain axis, which is a bidirectional neurohumoral communication system 
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between the gut and brain. The gut-brain axis has now been expanded to the 
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microbiota-gut-brain axis [14]. A growing body of evidence suggests that the 
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innumerable microorganisms that populate the mammalian gastrointestinal tract (gut 
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microbiota) are tightly linked to the aging process of their host [15, 16]. Age-related 
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changes in the bacterial composition of microbiota are well known, and alterations of 
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gut microbiota driven by diet may affect the health of elderly people [17, 18]. This 
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lack of knowledge is noteworthy because aging alone alters the gut microbiota 
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(dysbiosis) and likely contributes to age-related inflammation [19-22] , a condition 
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often referred to as “inflammaging”[23-25]. Importantly, age-related dysbiosis may be 
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a major contributor to the increasing prevalence of many age-related diseases and the 
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poorer outcomes observed in the elderly after an acute injury. 
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1.3 Microbiota-derived Metabolites 
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Dietary tryptophan metabolism is a complex pathway involving either 
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mammalian or bacterial enzymes or a combination of both. Dysregulation of this 
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tryptophan metabolism pathway and associations with metabolic disease is better 
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known for mammalian tryptophan derivatives. Bacterial enzymes (e.g., tryptophanase) 
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convert 
dietary 
tryptophan 
into 
indole. 
Indole 
can 
be 
metabolized 
to 
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indole-3-propionic acid (IPA) solely by bacteria. The role of these indole derivatives 
264 
in metabolic disease is not known. Gut microbiota have been reported to influence 
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tryptophan metabolism and serotonin biosynthesis[26, 27]. Another well known 
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microbiota-derived metabolite is short chain fatty acid (SCFA). Dietary fibers are 
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insoluble or soluble carbohydrates that are non-digestible by human digestive 
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enzymes. They are hydrolyzed and fermented in the colon through the action of 
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anaerobic bacteria, producing mostly SCFA. SCFA contributes to certain metabolic 
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processes, such as satiety increase, control of lipid synthesis in liver and fat storage, 
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increase of glucose uptake from small intestine and reduction of inflammation[28-30]. 
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1.4 Trace elements 
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Trace elements play an important role in maintaining metabolic hemostasis and 
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preventing many aged-associated diseases [31]. However, all trace elements are toxic 
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when ingested in excess amounts, yet many elements such as potassium, manganese, 
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and magnesium are necessary at trace levels for various bodily functions. Previous 
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studies indicated that long-term accumulations of non-essential elements may be 
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related to bone and muscle weakness [32-34]. Only one Korea study reported that 
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exposure to lead, cadmium, and mercury will increase the prevalence of sarcopenia in 
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the elderly population. This study data show that the odds ratio was increased with 
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increasing concentration quartile of lead (P<0.001), mercury (P<0.001) and cadmium 
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(P<0.001) in both genders, and the sum of the three heavy metals (P<0.001) in 
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females [35]. There is a limited report to study the association between trace elements 
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and sarcopenia in the elderly group. Therefore, the causality between trace elements 
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and sarcopenia should be verified in further longitudinal studies. 
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1.5 Regulatory T cells 
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Regulatory T cells (Tregs) are a crucial subset of immune cells responsible for 
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maintaining immune system homeostasis and preventing excessive immune responses. 
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These cells play a vital role in self-tolerance by suppressing the activity of other 
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immune cells, thus reducing the risk of autoimmune diseases. Tregs exert their effects 
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through the secretion of anti-inflammatory cytokines and direct interactions with other 
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immune cells to modulate immune responses. Recent research has highlighted the 
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significant impact of Tregs on muscle health and intestinal immunity. Studies have 
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shown that Tregs can modulate inflammatory factors to suppress muscle inflammation, 
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which contributes to muscle repair and regeneration. Additionally, beneficial gut 
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microbiota can induce Tregs to prevent intestinal inflammation and inhibit the 
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adhesion of pathogenic bacteria to the gut mucosa, thereby maintaining gut health [36, 
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37]. These findings underscore the essential role of Tregs in sustaining overall health 
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and mitigating chronic diseases. 
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1.6 Beneficial Effects of Lactobacillus casei strain Shirota (LcS) on GI Function, 
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Brain Function, and Inflammation 
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Lactobacillus casei strain Shirota (reclassified as Lacticaseibacillus paracasei 
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strain Shirota in April 2020) (LcS) is a well-known probiotic strain that has been 
304 
approved and is generally recognized as safe by the United States Food and Drug 
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Administration. LcS has been suggested to provide health benefits by balancing gut 
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microbiota, improving gastrointestinal dysfunction, preventing infection and cancer, 
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and modulating inflammatory and immune responses [38]. Previous studies have 
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demonstrated that LcS improves mood disturbances in the elderly [39] and decreases 
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anxiety symptoms in patients with chronic fatigue syndrome [40]. In a pilot trial, LcS 
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also suppressed the onset of physical symptoms in healthy students exposed to 
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academic stress [41]. However, studies have not examined fully whether LcS relieves 
312 
psychological stress-induced responses associated with the microbiota-gut-brain axis 
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of healthy subjects. 
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1.7 Preliminary Work 
315 
In our previous work, we investigated whether LcS decelerates age-related 
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muscle loss and its underlying mechanism with SAMP8 mice [42]. The body weight 
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and composition, muscle strength, protein intake, mitochondrial function, reactive 
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oxygen species, IL-6 and GI function were examined in this study. From the result we 
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found that LcS significantly attenuated decreases in muscle mass, holding impulse, 
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and grip force in 28-week-old female SAMP8 mice. Compared with the control group, 
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mice in the Sarcopenia with Fermented Milk containing Lactobacillus casei Strain 
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Shirota group (LcS group) also exhibited higher mitochondrial oxygen consumption 
323 
and biogenesis. Taken together, LcS may extenuated muscle lose during aging; this 
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effect may have been caused by preservation of the mitochondrial function via the 
325 
gut-muscle axis in SAMP8 mice. 
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327 
2. Study Objective 
328 
  We 
hypothesized 
that 
long-term 
Fermented 
Milk 
Containing 
LcS  
329 
supplementation would improve nutrient utilization and GI function, induce Treg cell 
330 
activation, and balance the inflammatory response in elderly Taiwanese individuals 
331 
with sarcopenia. These changes were expected to be associated with alterations in gut 
332 
microbial composition and microbial metabolites, ultimately impeding the progression 
333 
of sarcopenia in this population. The primary and secondary objectives were as 
334 
follows: 
335 
2.1 Primary objective 
336 
 
To assess changes in gut microbiota in elderly individuals with sarcopenia given 
337 
Fermented Milk Containing LcS for 12 weeks, The primary objective was to 
338 
confirm the efficacy of Fermented Milk Containing LcS in restructuring the gut 
339 
microbiota composition against aging-related gut dysbiosis in elderly individuals 
340 
with sarcopenia. 
341 
2.2 Secondary objective 
342 
 
To evaluate the efficacy of Fermented Milk Containing LcS intake in alleviating 
343 
reactive oxygen species (ROS) and changing biomarkers from baseline, 
344 
including TNF-α, TGF-β, IL-6, IL-10, and IL-17. 
345 
 
To understand the impact of Fermented Milk Containing LcS intake on impeding 
346 
the progression of muscle loss in elderly individuals with sarcopenia. 
347 
 
To investigate the correlations between Fermented Milk Containing LcS intake 
348 
and gut microbiota microbial metabolites, nutrient utilization states, trace 
349 
elements, GI function, and immune regulation in elderly individuals with 
350 
sarcopenia. 
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3. Study plan 
353 
3.1 Overall designs 
354 
This was an open label trial to examine the effects of Fermented Milk Containing 
355 
LcS supplementation on the elderly with sarcopenia. Subjects were between 65-85 
356 
years old and either male or female. A total of 120 subjects were enrolled. The study 
357 
design included the recruitment of 80 elderly individuals with sarcopenia, who were 
358 
randomly assigned to either the Fermented Milk Containing LcS supplement group 
359 
(LcS group) or the non-treatment group (SC group) in a 1:1 allocation ratio. 
360 
Additionally, 40 elderly individuals without sarcopenia were included as healthy 
361 
controls (NS group) for comparative analysis. The randomization process was 
362 
conducted using a lottery method to ensure balanced group allocation. Subject 
363 
participation was last 12 weeks. Subjects who were withdrawn from the study would 
364 
be replaced until 40 subjects per group. 
365 
3.2 Efficacy assessment 
366 
Efficacy of Fermented Milk Containing LcS supplementation was evaluated for 
367 
per-protocol (PP) analysis set population who completed the study by comparing 
368 
sarcopenia groups with and without Fermented Milk Containing LcS. Comparisons 
369 
were made in body composition, physical performance, gut microbiota composition, 
370 
microbiota metabolites, ROS, inflammation status, anabolic hormone level, immunity 
371 
evaluation, trace element level, and responses to various questionaries. 
372 
3.3 Primary and Secondary Endpoints 
373 
Primary Objective and Endpoint Alignment: 
374 
To ensure a comprehensive evaluation of Fermented Milk Containing LcS 
375 
supplementation, the study defined primary and secondary endpoints based on 
376 
existing literature and biological plausibility. The primary endpoint, gut microbiota 
377 
composition changes, was selected due to the established role of probiotics in 
378 
modulating microbial diversity and function. Gut dysbiosis has been implicated in 
379 
sarcopenia, making this a crucial parameter for assessing LcS efficacy. 
380 
The secondary endpoints included oxidative stress markers (ROS levels, GPx, and 
381 
CAT activity), inflammatory biomarkers (TNF-α, IL-6, IL-10, TGF-β), muscle mass 
382 
and physical performance, and trace element levels. These were chosen based on the 
383 
hypothesis that LcS supplementation could mitigate oxidative stress, inflammation, 
384 
and heavy metal accumulation, thereby influencing sarcopenia progression. The 
385 
selection of these endpoints aligns with previous findings that link gut microbiota to 
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metabolic and immune functions, which are critical in age-related muscle 
387 
degeneration. 
388 
4. Study intervention 
389 
4.1 Study supplementation and administration 
390 
Test beverages, Yakult 300 LIGHT Fermented Milk containing LcS at more than 
391 
3x1010 CFU per 100 mL bottle, were provided by Yakult Co., Ltd. (Taipei, Taiwan) 
392 
(Table 1). The beverages were sent to participant weekly (14 bottles/week) and stored 
393 
at temperatures ranging from 0–10°C. The subjects in the LcS supplemented group 
394 
consumed 2 bottles of the test beverages per day (104 kcal/day) at approximately the 
395 
same time.  
396 
The study protocol included rigorous adherence monitoring through weekly 
397 
follow-ups, where participants were required to return unused bottles and complete 
398 
adherence logs. Dietary records and questionnaires were also implemented to monitor 
399 
additional probiotic intake or dietary factors that could influence outcomes. 
400 
Additionally, compliance biomarkers, such as changes in gut microbiota profiles, 
401 
were analyzed post-intervention to validate adherence. 
402 
 
403 
Table 1. Nutrition fact of Yakult 300 LIGHT Fermented Milk 
404 
Yakult 300 LIGHT Fermented Milk per bottle (100 mL) 
Ingredients 
Content 
Calories 
52 kcal 
Fat 
0 g 
Protein 
1.3 g 
Carbohydrate 
14.5 g 
Sugar 
10.0 g 
Sodium 
17 mg 
Calcium 
60 mg 
Vitamin C 
28 mg 
Vitamin D 
0.9 μg 
Vitamin E 
3.4 mg 
Total number of LcS 
Over 3 x1010 CFU 
 
405 
4.2 Restriction and withdrawal 
406 
Subjects were prohibited to use other treatments for sarcopenia and discontinued 
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such treatments at least 4 weeks before the screening visit. Subjects were not allowed 
408 
to take the following items after screening and were withdrawn from the study if they 
409 
did not comply: 
410 
1. Probiotics, except for the allocated experimental drink 
411 
2. Antibiotics 
412 
3. Vitamin D or Ca 
413 
4. Oral or injected corticosteroid 
414 
5. Drug affecting the immune status 
415 
6. Chemotherapy 
416 
7. Structured physical exercise training program 
417 
Subjects were allowed to withdraw his or her consent at any time. The subjects 
418 
were monitored every 2 weeks to ensure the compliance of taking the experimental 
419 
drink. 
420 
 
421 
5. Subject Enrollment 
422 
The subjects were recruited from Taipei Medical University Hospital, Ministry 
423 
of Health and Welfare, Taipei Municipal Wanfang Hospital (Managed by Taipei 
424 
Medical University), and Community Integrated Care Service Center-Taipei 
425 
Community Care (Stone Soup) in Xinyi District commissioned by Taipei Medical 
426 
University. The definition of sarcopenia was based on the algorithm of Asian 
427 
Working Group for Sarcopenia (AWGS). 
428 
5.1 Inclusion Criteria  
429 
1. Age between 65 and 85 years 
430 
2. No use of hormonal replacement therapy 
431 
5.2 Exclusion Criteria 
432 
1. Active cancer: currently receiving cancer treatment or have received cancer 
433 
treatment within the last 3 months  
434 
2. Weight change ≥ 5% or weight change ≥ 5 kg within the past 3 months 
435 
3. BMI > 30 kg/m2  
436 
4. Disease requiring chronic use of prescription corticosteroids 
437 
5. History of ischemic or hemorrhage stroke  
438 
6. Unstable or uncontrollable hypertension (>180/110 mmHg) 
439 
7. Doing hemodialysis or peritoneal dialysis within the last 3 months 
440 
8. Participation in a structured physical exercise training program within the past 2 
441 

 
 
20260128 edited 
15 
 
year; previous use of creatinine supplementation; use of drugs that can affect bone 
442 
metabolism (e.g., glucocorticoids, bisphosphonates, vitamin D or calcium). 
443 
9. Antibiotics use in the past 3 months. 
444 
10. Products of probiotic use in the past 2 weeks. 
445 
11. Living abroad for one month in the past 3 months 
446 
12. Be hospitalized or have acute disease in the past 3 months 
447 
13. Abnormal thyroid function 
448 
14. Allergic to milk 
449 
5.3 Algorithm of recruitment and grouping 
450 
Participants who passed the exclusion criteria started the algorithm of 
451 
recruitment. In order to increase the efficiency of recruitment, two stages of 
452 
sarcopenia screening had been designed. The participants who passed step 1 
453 
proceeded with the step 2 screening as follows:  
454 
5.3.1 Step 1: First Screened cases  
455 
(1) Calf circumference: Calf circumference was measured by using tape. Low calf 
456 
circumference was defined as: 
457 
 
Men: <34cm 
458 
 
Women: <33cm 
459 
(2) Risk of sarcopenia: Risk of sarcopenia was measured by using SARC-F 
460 
questionnaire. High score was defined as: 
461 
 
Score: ≧4 
462 
5.3.2 Step 2: Second screened and confirm sarcopenia  
463 
(1) Skeletal muscle mass (SMI, Unit: mass (kg)/height (m2)) was measured by using 
464 
bioelectrical impedance analysis (BIA), and Low SMI was defined as 
465 
 
Men: SMI <7.0 kg/m2 
466 
 
Women: SMI <5.7 kg/m2     
467 
(2) Handgrip strength: Handgrip strength was measured by using electronic hand grip 
468 
dynamometer. Low handgrip strength was defined as: 
469 
 
Men: <28 kg 
470 
 
Women: <18 kg 
471 
(3) Limb strength: Limb strength was measured by using Time for 5 times for chair 
472 
stand method. Low limb strength was defined as:  
473 
 
Time for 5 times for chair stand: ≧12s 
474 
Sarcopenia was defined by (1) and one of (2) or (3) 
475 

 
 
20260128 edited 
16 
 
The participants who failed to pass the first screening were classified as 
476 
“non-sarcopenia group”. The participants of non-sarcopenia group only underwent 
477 
the baseline assessment.  The participants who were successful in the screening for 
478 
sarcopenia followed the randomization procedure and classified into “sarcopenia 
479 
group” and “LcS group”. Randomization was performed using a lottery method to 
480 
allocate participants at 1:1 ratio (Figure 1).  
 
481 

 
 
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17 
 
5.3.3 Workflow of participant’s recruitment and grouping 
482 
 
483 
Figure 1. Workflow of participant’s recruitment and grouping  
 
484 

 
 
20260128 edited 
18 
 
6. Material and method 
485 
6.1 Demographic data and medical history 
486 
Demographic data (included basic information, nutritional assessment, food 
487 
frequency ingestion, daily personal habits, physical activity and oil-smoke exposure 
488 
assessment etc.) and a complete medical history were collected at the screening visit. 
489 
The medical histories were traced back for 3 months. Meanwhile, the history of 
490 
structured physical exercise training program was traced back to past 2 years before 
491 
screening.  
492 
6.2 Anthropometric measurement and Body Composition Assessment 
493 
Anthropometric measurements, including height, weight, waist circumference, 
494 
arm muscle circumference, hip circumference, and calf circumference, were 
495 
determined using a measure tape and weight scale. Body composition was determined 
496 
by bioelectrical impedance analysis (BIA).  
497 
6.2.1 Bioelectrical impedance analysis (BIA) 
498 
Participants stood on floor mat without shoes and socks. Arms did not touch the 
499 
trunk part of body, and were spread naturally to a 15 degree angle away from trunk. 
500 
The hand electrodes were marked THUMB for the thumb and MIDDLE for the 
501 
middle finger. The foot electrodes were positioned between examinee’s anklebone 
502 
and heel. Electric current was supplied from the electrodes on the tips of the heels of 
503 
both feet and the fingertips of both hands. 
504 
6.3 Muscle strength 
505 
Grip strength was measured under standard conditions with a well-studied model 
506 
of a handheld dynamometer with reference populations could be a reliable surrogate 
507 
for more complicated measures of muscle strength in the lower arms or legs. The 
508 
measurement used electronic hand grip dynamometer. Low handgrip strength was 
509 
defined as Men: <28 kg; Women: <18kg. Participants sat and stood five times, and 
510 
time was calculated using a timer. Low limb strength was defined as time for 5 times 
511 
chair stand: ≧12s. 
512 
6.4 Physical performance  
513 
Physical performance was determined by using 6-meter usual gait speed, timed 
514 
Up and Go (TUG) test, and Short Physical Performance Battery (SPPB). 
515 
6.4.1 Walk speed 
516 
Participants were instructed to walk from a standing start at a pace that was 
517 
normal and comfortable for them or to walk as fast as they could until they reached 
518 

 
 
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the end of the marked path. A trained examiner walked behind the participant and 
519 
stopped timing when the participant’s foot contacted the floor at the end of the 
520 
walking course. Participants were provided rest breaks as needed throughout the 
521 
testing session. Low physical performance was defined < 1 m/s. 
522 
6.4.2 TUG 
523 
Participants were instructed to stand up from the chair, walk 3 meters forward 
524 
and go back to sit on the chair. Low physical performance was defined ≧ 20s. 
525 
6.4.3 SPPB 
526 
Participants were followed to SPPB to do the test. Low physical performance 
527 
was defined ≦ 9 pt (AppendixⅠ). 
528 
6.5 Gut microbiota composition and microbiota-derived metabolite analysis 
529 
6.5.1 Fecal sample collection and bacterial genomic DNA isolation 
530 
  Fresh fecal samples were collected from individual participant. Participants were 
531 
instructed to place toilet paper at the front of the toilet bowl before defecation to 
532 
prevent fecal contamination with urine or water. They were asked to defecate in the 
533 
opposite direction of their usual practice, ensuring the feces were deposited onto the 
534 
toilet paper. Using the collection stick provided in the sampling kit, participants 
535 
collected fecal samples and transferred them into the provided tubes. One of the tubes 
536 
contained a preservation solution, which required thorough mixing after sample 
537 
collection. Both tubes were then stored in a -80 °C freezer.  
538 
  The genomic DNA was extracted by using the QIAamp DNA Stool Mini kit 
539 
according to the manufacturer’s protocol. DNA concentrations were measured by 
540 
using NanoDrop2000. The fecal samples were stored at -80°C refrigerator.  
541 
6.5.2 16S rRNA amplicon and data analysis 
542 
The V1-V9 region of the 16S rRNA gene by PCR were referred to the Illumina 
543 
16S Metagenomic Sequencing Library Preparation protocol. The amplicons were 
544 
paired and sequenced on an Illumina MiSeq 2000 platform according to 
545 
manufacturer’s protocol. The data was analyzed with Quantitative Insights into 
546 
Microbial Ecology 2 (QIIME2) with NCBI 16S RNA database (2023/01). The 
547 
UPARSE algorithm was used to align with identical sequences and determine the 
548 
representative operational taxonomic unit (OTU) sequences. For sequences with 97% 
549 
similarity, they were clustered as same OTU. Alpha diversity analysis (Shannon index) 
550 
was performed using QIIME2 while beta diversity analysis was analyzed using 
551 
weighted principal coordinate analysis (PCoA). The online Galaxy workflow 
552 

 
 
20260128 edited 
20 
 
framework was used to determine the linear discriminant analysis (LDA) effect size 
553 
(LEfSe). 
554 
6.5.3 Fecal Short Chain Fatty Acids (SCFAs), IPA and TMAO Determination 
555 
The fecal samples were diluted in 0.5% phosphoric acid and homogenized. After 
556 
centrifuge, the supernatant was mixed with 4-ethylpentanoic acid in ethyl acetate, and 
557 
kept the supernatant at -20°C.  
558 
The untargeted metabolomics was performed using HPLC coupled with a 
559 
Q-Exactive Orbitrap Plus mass spectrometer (Thermo Fisher Scientific, Bremen, 
560 
Germany) as previously described[43, 44]. Liquid chromatography was performed on 
561 
HPLC system using a BEH C18 column (1.7 μM, 2.1 mm x 100 mm, Waters 
562 
Corporation). The injection volume was 2 μL with a flow rate of 0.3 mL/min and the 
563 
column temperature was maintained at 35°C. HPLC linear gradient conditions were: 
564 
0–0.5 min 1 % B, 0.5–2.5 min from 1 % B to 10 % B, 2.5–3.5 min 65 % B, 3.5–5.0 
565 
min from 35 % B to 100 % B, and 5.0–9 min 1 % B [solvent system A: water/formic 
566 
acid (100:0.1, vol/vol); B: acetonitrile/formic acid (100:0.1, vol/vol)]. The capillary 
567 
temperature and voltage (+ and -) were maintained at 275 °C and 3,600 and 3,200 V, 
568 
respectively. The samples for quality control (QC) were prepared by mixing all the 
569 
samples. Data were acquired by Thermo Data Analysis software Compound 
570 
Discoverer TM2.0. The raw intensities were transformed and normalized. For 
571 
multiple peaks mapped to the same metabolites, the average intensity value was used. 
572 
The matrix was then subjected to principal component analysis (PCA) and orthogonal 
573 
partial least squares discriminant analysis (OPLS-DA) using the R ropls package 
574 
(version 1.21.0) to obtain differential metabolites between groups. 
575 
6.6 Analysis of blood biochemical parameters  
576 
6.6.1 Whole blood, serum and plasma samples preparation 
577 
Blood samples were collected from the subjects at pre-test and post-test. Whole 
578 
blood was placed in collection tubes containing K2EDTA and then stored at 4°C until 
579 
analysis. For plasma preparation, blood samples were placed in collection tubes 
580 
containing heparin and centrifuged at 3,000 g for 10 min then took supernatant. For 
581 
serum preparation, blood samples were placed in collection tubes and then centrifuged 
582 
at 3,000 g for 10 min then took supernatant. The plasma and serum were stored at 
583 
-70°C until analysis. 
584 
6.6.2 Biochemical parameters 
585 
All biochemical parameters were performed one day before the start of the 
586 

 
 
20260128 edited 
21 
 
intervention and at the end of intervention. Albumin, prealbumin, cholesterol, HDL-C, 
587 
LDL-C, triglyceride, TSH, free T4, hsCRP, HbA1c (%), fasting glucose, fasting 
588 
insulin, HOMA-IR, ALT, AST, creatinine, eGFR, WBC, RBC, HGB, HCT, MCHC, 
589 
platelet and RDW-CV were determined after fasting for 8 hrs. Normal values of 
590 
biochemical parameters were shown in Table 2. 
591 
Table 2. Normal value of biochemical parameters 
592 
Item 
Normal value 
TSH (μIU/ mL) 
0.35-5.50 μIU/mL 
Free T4 (ng/dL) 
0.76~1.64 ng/dL 
hsCRP (mg/dL) 
<1.0 mg/L 
Albumin (g/dL) 
3.5-5.5 g/dL 
Pre-albumin (mg/dL) 
20-40 mg/dL 
CHOL (mg/dL) 
<200 
HDL-C (mg/dL) 
M:>40 mg/dL；F:>50 mg/dL 
LDL-C (mg/dL) 
<100(Desirable) 
TG (mg/dL) 
<150 mg/dL 
HbA1c (%) 
4.0~5.6% 
GLU(AC) (mg/dL) 
70~99 mg/dl 
insulin (μIU/mL) 
1.5-17.0 μU/mL 
HOMA-IR 
HOMA-IR ≦ 1.4 
GOT(AST) (U/L) 
0-34 U/L 
GPT(ALT) (U/L) 
0-36 U/L 
BUN (mg/dL) 
6-21 mg/dL 
CREA (mg/dL) 
M:0.64~1.27 mg/dL；F:0.44~1.03 mg/dL 
eGFR (mL/min/1.73 m^2) ≧ 60 mL/min/1.73 m2 
WBC (1000/μl) 
M:3.9-10.6 1000/μL；F:3.5-11.0 1000/μL 
RBC (million/μl) 
M:4.5-5.9 Million/μL；F:4.0-5.2 Million/μL 
HGB (g/dL) 
M:13.0~18.0 gm/dl；F:11.0~16.0 gm/dl 
HCT (%) 
M:41-53%；F:36-46% 
MCHC (g/dL) 
31-37 gHb/dL 
Platelet (1000/μL) 
150-400 1000/μL 
RDW-CV (%) 
11.5-14.5% 
 
593 
6.6.3 Analysis of pro-inflammatory and anti-inflammatory cytokines 
594 

 
 
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Pro-inflammatory cytokines (IL-6 and TNF-α) and anti-inflammatory cytokines 
595 
(IL-10, IL-17 and TGF-β) were determined by using ELISA Kits (Elabscience 
596 
Biotechnology Inc) via ELISA reader (BioTek, PowerWave XS2, City, State, USA). 
597 
6.6.4 Analysis of antioxidant enzymes  
598 
Superoxide dismutase (SOD) (E-BC-K019, Elabscience Biotechnology Inc.), 
599 
Glutathione peroxidase (GPx) (E-BC-K096, Elabscience Inc.) and Catalase (CAT) 
600 
(E-BC-K031, Elabscience Biotechnology Inc.) are antioxidant enzymes in human. 
601 
Enzymatic activity in serum was determined by using ELISA Kits via ELISA reader 
602 
(BioTek, PowerWave XS2, City, State, USA). 
603 
6.6.5 Determination of blood metal elements  
604 
One mL of whole blood samples were microwave digested with 3 mL of 65% 
605 
nitric acid (Ultrapure Reagent, J.T. Baker). Subsequently, we washed the residuals in 
606 
microwave tubes with 2% nitric acid and then filtered the digested fluids with 0.22 
607 
μm filter. The total filtered solutions were stored in 15 mL centrifuge tubes. The 
608 
levels of Na, Mg, K, Mn, Fe, Ni, Cu, Zn, Sr, As, Se, Mo, Cd, Sb, Ba and Pb were 
609 
determined by using inductively coupled plasma mass spectrometry (ICP-MS; Agilent 
610 
7800). 
611 
6.6.6 Analysis of anabolic hormone 
612 
Age-dependent muscle breakdown resulted in the decrease of anabolic hormones; 
613 
therefore, dehydroepiandrosterone sulfate (DHEA-S) was employed in this study. 
614 
Serum DHEA-S was determined by using ELISA kit (AB108669) via ELISA reader 
615 
(Bio Tek, PowerWave XS2, City, State, USA). 
616 
6.6.7 Analysis of immune function 
617 
Human peripheral bloods were separated from whole blood by using PluriMate 
618 
(#44-91002-11). After separation, Fc receptors in PBMC were blocked by Fc blocker 
619 
(Biolegend #422302), stained with monoclonal antibodies (CD3-EV450, CD4-AF488, 
620 
CD8a-AF647, CD25- PE/Cyanine7, (Elabscience Biotechnology Inc.)), and analyzed 
621 
by the Invitrogen™ Attune™ NxT acoustic focusing cytometer (Thermo Fisher 
622 
Scientific). 
623 
6.7 Questionnaire 
624 
6.7.1 Basic information 
625 
The researchers with IRB certificate helped subjects to fill their self-reported 
626 
basic information, including age, disease history, etc. The reporting form was shown 
627 
in Appendix II. 
628 

 
 
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23 
 
6.7.2 24-Hour dietary recall 
629 
24-Hour dietary recall was to recall past 24 hours food intake and record food 
630 
items, cooking methods, portion sizes, and calorie calculation. The reporting form was 
631 
shown in appendix III. 
632 
6.7.3 International physical activity questionnaire (iPAQ) 
633 
iPAQ was used to estimate the types of intensity of physical activity and sitting 
634 
time. The reporting form was shown in appendix IV. 
635 
6.7.4 Food frequency questionnaire 
636 
Food frequency questionnaire was used to estimate long-term dietary conditions. 
637 
The reporting form was shown in appendix V. 
638 
6.7.5 Taiwan lifestyle assessment 
639 
Lifestyle questionnaire as a thirteen-item questionnaire which was reported by 
640 
patients themselves. The reporting format was shown in appendix VI. 
641 
6.7.6 Patient Assessment of Constipation symptom (PAC-SYM) 
642 
PAC-SYM was used to measure specific symptoms of patients with constipation, 
643 
was developed in parallel with a complementary instrument to comprehensively 
644 
measure disease QoL outcomes (PAC-QOL). These instruments could be used 
645 
separately or together. The reporting format was shown in appendix VII 
646 
6.7.7 SARC-F questionnaire 
647 
SARC-F was a five-item questionnaire, which was reported by patients 
648 
themselves to screen for sarcopenia. The reflection was based on the patient's 
649 
perception of his own strength, ability to walk, stand up from the chair, climb stairs 
650 
and fall experience. More than 4 points might be at risk of sarcopenia. The reporting 
651 
format was shown in appendix VIII. 
652 
6.7.8 Mini nutrition assessment (MNA) 
653 
MNA was used to screen out high-risk groups of malnutrition. The maximum 
654 
score obtained by MNA was 30 points, between 17-23.5 represents the risk of 
655 
malnutrition, less than 17 points was malnutrition. The reporting format was shown in 
656 
appendix IX. 
657 
6.8 Statistical analysis  
658 
The required sample size was determined using G*Power software, ensuring 
659 
adequate power to detect meaningful differences. The calculation was based on an 
660 
estimated effect size of 0.5 (moderate effect), power of 80%, and an alpha level of 
661 
0.05, following recommendations for microbiome and clinical intervention studies. 
662 

 
 
20260128 edited 
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The sample size estimate also accounted for potential dropouts, ensuring robustness in 
663 
statistical analysis. Randomization was conducted using a computer-generated 
664 
sequence, stratified by baseline sarcopenia severity to ensure balanced group 
665 
allocation. Data were presented as median (IQR), number (percentage) or 
666 
box-and-whisker plots (min-max). Comparisons between two groups were performed 
667 
with Mann Whitney test or Wilcoxon matched-pairs signed rank test, while 
668 
comparisons across more than two groups were performed with Kruskal-Wallis test 
669 
followed by Dunn’s post hoc test. Demographic data were analyzed using the 
670 
Chi-squared test (χ2 test) or Fisher exact test. A p-value of <0.05 was considered 
671 
significantly significant. 
 
672 

 
 
20260128 edited 
25 
 
7. Results 
673 
 
674 
7.1 Sarcopenia vs healthy subjects: Participants evaluation 
675 
7.1.1 Basic characteristics   
676 
On the basis of sarcopenia criteria, a total of 92 screened participants were eligible 
677 
for sarcopenia, and a total of 40 screened participants were eligible for non-sarcopenia 
678 
(Figure 2). Study subjects were enrolled from October 1st 2021 and the last subject 
679 
completed their 12-weeks treatment on January 13th 2023. 12 subjects were 
680 
withdrawn during 12-weeks treatment. The PP analysis included all study subjects (n 
681 
=120) who were randomized to NS, SC, LcS groups. The baseline characteristics of 
682 
SC and LcS groups were well balanced (Table 3). 
683 
The basic information form and lifestyle habits questionnaire enabled us to 
684 
comprehend the fundamental living conditions of the participants, providing a 
685 
standard for evaluating subsequent data more effectively. 
686 
Results from Table 4 indicated that there were no statistically significant 
687 
differences in age, gender, education level, annual income, or medical history between 
688 
the sarcopenia group (SC) and the control group (NS), as indicated by p-values 
689 
greater than 0.05. 
690 
Data from lifestyle on demographic characteristics (Table 5) revealed that the 
691 
consumption of coffee, tea, and vitamin supplements showed no statistically 
692 
significant differences between the SC and the NS, with p-values exceeding 0.05. 
693 
Conversely, alcohol consumption exhibited a statistically significant difference 
694 
between the SC and the NS, with a higher consumption rate in the NS group, as 
695 
evidenced by a p-value less than 0.05. In terms of environmental exposure 
696 
assessments, there were no statistically significant differences between the groups in 
697 
exposure to kitchen oil fumes, usage frequency of exhaust fans, frequency of incense 
698 
burning (including sandalwood and other aromatics), smoking frequency, and the 
699 
probability of exposure to secondhand smoke, all showing p-values greater than 0.05. 
700 
However, a statistically significant difference was noted in the frequency of mosquito 
701 
coil use between the SC and the NS, with less usage in the SC group compared to the 
702 
NS group (p = 0.028). 
703 
While the statistical results indicated significant differences in alcohol consumption 
704 
and mosquito coil usage frequency, the majority of participants either did not 
705 
consume alcohol or use mosquito coils. Consequently, these findings does not support 
706 

 
 
20260128 edited 
26 
 
the conclusion that the sarcopenia group has a lower frequency of alcohol 
707 
consumption or mosquito coil usage compared to the control group. 
708 
 
709 
7.1.2 Anthropometric measurement  
710 
Sarcopenia patients generally exhibited lower muscle mass compared to 
711 
non-sarcopenic individuals. Consequently, this study employed the InBody S10 to 
712 
assess intergroup differences in body composition, aiming to confirm the specific 
713 
body composition disparities between sarcopenic subjects (SC) and control subjects 
714 
(NS). 
715 
Data from Table 6 on body composition revealed that subjects diagnosed with 
716 
sarcopenia exhibited significantly reduced values in several parameters compared to 
717 
control subjects. These parameters included waist circumference, body weight, Body 
718 
Mass Index (BMI), Skeletal Muscle Mass Index (SMI), skeletal muscle mass, soft 
719 
lean mass, fat-free mass, protein content, fat content, intracellular water, extracellular 
720 
water, total body water, and whole body phase angle. Collectively, the data indicate 
721 
that subjects in the sarcopenia group not only meet the clinical criteria for sarcopenia 
722 
but also exhibit statistically significant differences from the control group in various 
723 
measured parameters.   
724 
 
725 
7.1.3 Physical performance 
726 
  The physical performance of elderly individuals was impacted by the natural 
727 
decline of bodily functions associated with aging, which could lead to chronic 
728 
diseases. Physical performance tests were essential for rapidly assessing a subject's 
729 
capabilities in daily living, exercise, and physical activities. This study utilized a 
730 
battery of tests, including grip strength measurement, chair stand test, walking speed 
731 
assessment, the TUG test, and the SPPB score, to evaluate the physical performance 
732 
of the subjects. Additionally, the International Physical Activity Questionnaire (IPAQ) 
733 
was administered to assess the usual physical activity levels of the subjects. 
734 
  In the results of the physical performance assessment (Table 7), individuals 
735 
diagnosed with sarcopenia showed no statistically significant differences in the chair 
736 
stand test, walking speed, or the TUG test compared to the control group. However, 
737 
the sarcopenia group demonstrated significantly lower grip strength and SPPB scores 
738 
relative to controls. Additionally, as outlined in Table 8, there are no statistically 
739 
significant differences in physical activity levels between the sarcopenia group and 
740 

 
 
20260128 edited 
27 
 
the control group. 
741 
 
742 
7.1.4 Dietary Assessment (MNA, 24h-recall, and Food frequency questionnaire) 
743 
  This study employed the Mini Nutritional Assessment (MNA), the 24-hour dietary 
744 
recall method, and the food frequency questionnaire (FFQ) to evaluate the nutritional 
745 
status of the subjects.  
746 
   Data from Table 9, utilizing the Mini Nutritional Assessment (MNA), indicated a 
747 
statistically significant difference between the SC and the NS, with more 
748 
well-nourished participants in the NS group compared to the SC group (p = 0.002). 
749 
Results from the 24-hour dietary recall presented in Table 10 showed no 
750 
statistically significant differences in total caloric intake, carbohydrate, protein, and 
751 
fat intake percentages between the SC and the NS, all with p-values greater than 0.05. 
752 
According to Table 11, which documents dietary intake frequencies, there were 
753 
no significant differences in the frequencies of fish and deep-sea fish consumption 
754 
between the SC and the NS, as indicated by p-values greater than 0.05. However, a 
755 
statistically significant difference waswas observed in the frequency of other seafood 
756 
consumption between the SC and the NS, with higher consumption the NS group (p = 
757 
0.019). No significant differences were noted in meat consumption frequencies 
758 
between the two groups (p>0.05). Statistically significant differences were present in 
759 
the frequencies of consumption of animal organs or blood and processed meats 
760 
between the SC and the NS, with higher consumption in the SC group, both with 
761 
p-values less than 0.05. No significant difference was found in the frequency of 
762 
legume product consumption between the groups (p>0.05). 
763 
  Based on the results, there was no difference in total caloric intake between the SC 
764 
group and the NS group. However, the SC group might be at risk of malnutrition. The 
765 
food frequency questionnaire indicate that the SC group has a lower intake frequency 
766 
of seafood, animal offal or blood, and processed meat products. 
767 
 
768 
7.1.5 Patient Assessment of Constipation symptom 
769 
  Current research suggested that gastrointestinal symptoms might have systemic 
770 
effects. The constipation self-assessment scale could evaluate participants' abdominal 
771 
symptoms (including stomach pain, abdominal cramping, bloating, and discomfort), 
772 
rectal symptoms (including decreased frequency of bowel movements, straining 
773 
during defecation, painful defecation, sensation of incomplete evacuation, difficulty 
774 

 
 
20260128 edited 
28 
 
passing stool despite the urge, rectal bleeding or tearing, and rectal burning sensation), 
775 
and stool hardness. Utilizing this scale could provide a preliminary understanding of 
776 
whether sarcopenia patients commonly exhibited abnormal gastrointestinal symptoms. 
777 
  Results from the constipation self-assessment (PAC-SYM) detailed in Table 12 
778 
revealed no statistically significant differences in the abdominal symptom scores 
779 
(encompassing stomach pain, abdominal cramps, abdominal distension, and general 
780 
abdominal discomfort) between the SC and the NS, with p-values exceeding 0.05. 
781 
Additionally, the rectal symptom scores, which included variables such as decreased 
782 
defecation frequency, effortful defecation, painful defecation, sensation of incomplete 
783 
bowel clearance, urge to defecate without success, rectal bleeding or fissure, and 
784 
rectal burning, similarly showed no statistically significant differences between the 
785 
SC and the NS, with all p-values greater than 0.05. No significant differences were 
786 
observed in stool hardness scores between the two groups (p>0.05). Furthermore, the 
787 
total scores for the non-intervention group (SC) compared to the NS also indicated no 
788 
significant discrepancies (p>0.05). 
789 
 Based on the above results, there are no difference in gastrointestinal function 
790 
between sarcopenia patients and non-sarcopenic individuals. 
791 
 
792 
7.1.6 Blood biochemical parameters 
793 
Blood biochemical analysis was performed to identify potential differences in 
794 
biochemical functions between sarcopenia patients and non-sarcopenic individuals, 
795 
providing a foundation for subsequent experiments. 
796 
Analysis of the blood biochemistry data presented in Table 13 indicated that there 
797 
were no statistically significant alterations in the following biochemical markers for 
798 
both the sarcopenia group and the control group: Thyroid-Stimulating Hormone 
799 
(TSH), Free Thyroxine (Free T4), High Sensitivity C-Reactive Protein (hsCRP), 
800 
Albumin, Pre-albumin, Total Cholesterol (CHOL), High-Density Lipoprotein 
801 
Cholesterol (HDL-C), Low-Density Lipoprotein Cholesterol (LDL-C), Triglycerides 
802 
(TG), Hemoglobin A1c (HbA1c), Glucose (GLU AC), Insulin, Homeostatic Model 
803 
Assessment for Insulin Resistance (HOMA-IR), Aspartate Aminotransferase (AST), 
804 
Alanine Aminotransferase (ALT), Blood Urea Nitrogen (BUN), Creatinine, Estimated 
805 
Glomerular Filtration Rate (eGFR), White Blood Cells (WBC), Red Blood Cells 
806 
(RBC), Hemoglobin (HGB), Hematocrit (HCT), Mean Corpuscular Hemoglobin 
807 
Concentration (MCHC), Platelets, and Red Cell Distribution Width-Coefficient of 
808 

 
 
20260128 edited 
29 
 
Variation (RDW-CV). 
809 
Based on the above results, there are no significant differences in blood biochemical 
810 
parameters between sarcopenia patients and non-sarcopenic individuals. 
811 
 
812 
7.1.7 T cell subtypes 
813 
T cells played an essential role in skeletal muscle repair and fibrosis. Research had 
814 
shown a positive correlation between higher levels of CD4+ T cells and the 
815 
prevalence of sarcopenia[45, 46]. In this study, the analysis of T lymphocyte subsets 
816 
(Table 14) revealed no significant differences in the proportions of CD3+, CD8+, and 
817 
regulatory T cells between the SC and the NS (p>0.05). However, the sarcopenia 
818 
group exhibited a significantly higher proportion of CD4+ T cells compared to the 
819 
control group (p<0.05). Therefore, the proportion of CD4+ T cells may serve as a 
820 
potential biomarker for the diagnosis of sarcopenia.  
821 
 
822 
7.1.8 Pro-inflammatory and anti-inflammatory cytokines 
823 
  Current research predominantly indicated that age-induced inflammatory responses 
824 
are a primary cause of sarcopenia. Therefore, we measured common inflammatory 
825 
cytokines, such as IL-6, TNF-α, IL-10, TGF-β, and IL-17, to evaluate their roles in 
826 
this process. 
827 
Table 15 result revealed that individuals in the sarcopenia group exhibited 
828 
significantly elevated levels of pro-inflammatory cytokines (e.g., IL-6, TNF-α) 
829 
relative to the control group. Conversely, concentrations of anti-inflammatory 
830 
cytokines (e.g., IL-10, TGF-β) were significantly reduced in the sarcopenia group. It 
831 
was noteworthy that the level of the pro-inflammatory cytokine IL-17 was 
832 
significantly lower in the sarcopenia group compared to the control group. 
833 
 
834 
7.1.9 Antioxidant activity 
835 
  The activity of antioxidant enzymes in the body decreased with aging, and 
836 
age-related oxidative stress might contribute to muscle damage. Consequently, this 
837 
study assessed the activity of antioxidant enzymes in the blood to evaluate whether 
838 
sarcopenia patients exhibited reduced antioxidant capacity. The data on antioxidant 
839 
enzyme activity (Table 16) revealed that, except for superoxide dismutase (SOD), 
840 
which showed no significant difference between the two groups (p>0.05), the SC had 
841 
significantly lower levels of catalase (CAT) and glutathione peroxidase (GPx) 
842 

 
 
20260128 edited 
30 
 
compared to the NS (p<0.05). These findings indicate that the sarcopenia group has a 
843 
generally reduced antioxidant enzyme activity relative to the control group, with 
844 
significant differences specifically in CAT and GPx levels (p<0.05). 
845 
 
846 
7.1.10 Anabolic hormone 
847 
   Dehydroepiandrosterone sulfate (DHEA-S) has anti-aging effects, but its secretion 
848 
declines with age. Therefore, by measuring the blood levels of DHEA-S, we could 
849 
determine whether sarcopenia patients (SC) exhibited a greater tendency towards 
850 
aging compared to the NS. The data on bone anabolic hormones (Table 17) indicate 
851 
that DHEA-S levels are lower in the sarcopenia group compared to the control group, 
852 
although this difference is not statistically significant (p=0.082). 
853 
 
854 
7.1.11 Metal concentration 
855 
Analysis of the blood metal concentrations detailed in Table 18 reveals 
856 
significant elevations in nickel (Ni) and antimony (Sb) levels in the sarcopenia group 
857 
compared to the control group. Conversely, selenium (Se) and lead (Pb) 
858 
concentrations were significantly lower in the sarcopenia group. No statistically 
859 
significant differences were observed in the levels of sodium (Na), magnesium (Mg), 
860 
potassium (K), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), strontium (Sr), 
861 
arsenic (As), molybdenum (Mo), cadmium (Cd), and barium (Ba) between the two 
862 
groups. 
863 
 
864 
7.1.12 Gut microbiota 
865 
Alpha diversity analysis based on the Shannon index (Fig. 3A) and Observed 
866 
features index (Fig. 3B) showed no significant differences between the NS and SC 
867 
groups (p > 0.05). However, β-diversity analysis using Bray–Curtis distance (Fig. 3C) 
868 
demonstrated partial separation between the two groups (PERMANOVA, p < 0.05), 
869 
indicating differences in overall gut microbial community structure. 
870 
The taxonomic composition of the gut microbiota was evaluated at the genus 
871 
level. Figure 3D shows average relative abundances of the 20 most abundant bacterial 
872 
genera in both groups, while Figure 3F displays the relative abundances of the top 35 
873 
bacterial genera in each subject. 
874 
Genus-level comparisons identified distinct bacterial signatures associated with 
875 
sarcopenia status (Fig 3E, Fig. 4, Table 19). Both LEfSe (Fig 3E) and 
876 

 
 
20260128 edited 
31 
 
MetagenomeSeq analyses (Fig 4) revealed significantly lower abundance of 
877 
Bifidobacterium in SC compared to NS. LEfSe-only findings included increased 
878 
Shigella 
and 
Muribaculum, 
and 
decreased 
Catenibacterium 
in 
SC. 
879 
MetagenomeSeq-only results showed enrichment of Anaerobutyricum, Anaerofilum, 
880 
Gluceribacter, Lachnospira, and Oscillibacter, along with depletion of Duncaniella, 
881 
Subdoligranulum, and Tyzzerella in SC. 
882 
Collectively, while microbial richness and evenness were comparable, SC 
883 
participants exhibited reduced beneficial taxa such as Bifidobacterium and 
884 
Subdoligranulum, and increased abundance of genera potentially linked to dysbiosis 
885 
or 
inflammation 
(Shigella, 
Oscillibacter, 
Anaerobutyricum), 
suggesting 
a 
886 
sarcopenia-associated gut microbial shift. 
887 
 
888 
7.2 Influence of Fermented Milk Containing Lactobacillus casei 
889 
Strain Shirota intervention in Sarcopenia elderly 
890 
7.2.1 Anthropometric measurement  
891 
This study utilized the InBody S10 to evaluate the body composition of participants. 
892 
The comparison of pre- and post-intervention body measurements in the SC (Table 20) 
893 
revealed no significant differences in waist circumference, height, weight, BMI, 
894 
skeletal muscle mass, soft lean mass, fat-free mass, protein, fat, intracellular water, 
895 
and extracellular water (p>0.05). However, significant increases were observed in 
896 
SMI, total body water, and WBPA (p<0.05). In the LcS group, the comparison of pre- 
897 
and post-intervention body measurements (Table 20) showed no significant 
898 
differences in waist circumference, height, weight, BMI, SMI, skeletal muscle mass, 
899 
soft lean mass, fat-free mass, protein, fat, intracellular water, extracellular water, total 
900 
body water, and WBPA (p>0.05). When comparing the post-intervention body 
901 
measurements between the SC and the LcS group (Table 20), no significant 
902 
differences were found in waist circumference, height, weight, BMI, SMI, skeletal 
903 
muscle mass, soft lean mass, fat-free mass, protein, fat, intracellular water, 
904 
extracellular water, total body water, and WBPA (p>0.05). Therefore, the Fermented 
905 
Milk Containing LcS intervention does not appear to be effective in improving the 
906 
body composition of sarcopenia patients. 
907 
 
908 
7.2.2 Physical performance 
909 
This study utilized a series of assessments to evaluate physical performance, including 
910 

 
 
20260128 edited 
32 
 
grip strength, the chair stand test, walking speed, the Timed Up and Go (TUG) test, 
911 
and the SPPB score. The International Physical Activity Questionnaire (IPAQ) was 
912 
also administered to gauge participants' usual physical activity levels. In the SC, pre- 
913 
and post-test comparisons of physical performance measurements (Table 22) showed 
914 
no significant differences in grip strength, chair stand test, walking speed, TUG test, 
915 
and SPPB score (p>0.05). Conversely, in the LcS group, significant improvements 
916 
were observed in grip strength, chair stand test, walking speed, TUG test, and SPPB 
917 
score after 12 weeks of intervention (p<0.05). Further comparison of post-test 
918 
physical performance between the SC and the LcS group revealed no significant 
919 
differences in grip strength and the TUG test (p>0.05). However, significant 
920 
improvements were noted in the chair stand test, walking speed, and SPPB score in 
921 
the LcS group (p<0.05). Regarding physical activity levels, no significant differences 
922 
were found in pre- and post-test comparisons (Table 22) within either the SC or LcS 
923 
groups, nor in comparisons between the SC and LcS groups (p>0.05). These findings 
924 
suggest that, while the Fermented Milk Containing LcS intervention did not enhance 
925 
muscle mass, it led to significant improvements in physical performance metrics in 
926 
sarcopenia patients without affecting their overall physical activity levels. 
927 
 
928 
7.2.3 Dietary Assessment (MNA, 24h-recall)  
929 
  This investigation utilized the Mini Nutritional Assessment (MNA) and a 24-hour 
930 
dietary recall to evaluate the nutritional status of participants. According to results 
931 
from Table 23, the Mini Nutritional Assessment showed no significant changes in 
932 
nutritional intake between pre- and post-assessment within the sarcopenia 
933 
non-intervention group (SC) (p>0.05). Conversely, significant differences in 
934 
nutritional intake were noted within the LcS group between the pre- and 
935 
post-assessments (p<0.05). No statistically significant differences were found in 
936 
post-assessment nutritional status between the sarcopenia non-intervention group (SC) 
937 
and the LcS group (p>0.05). 
938 
  Data from Table 24, derived from the 24-hour dietary recall, indicated no 
939 
significant alterations in total calorie consumption, carbohydrate intake percentage, 
940 
protein intake percentage, or fat consumption during pre- and post-intervention 
941 
comparisons within the sarcopenia non-intervention group (SC) (p>0.05). Similarly, 
942 
no significant differences were observed within the LcS group in the mentioned 
943 
dietary metrics between pre- and post-intervention (p>0.05). Additionally, 
944 

 
 
20260128 edited 
33 
 
comparative analysis of dietary nutrition status in post-intervention assessments 
945 
between the two groups also revealed no significant differences (p>0.05)." 
946 
 
947 
7.2.4 Constipation symptom assessment 
948 
  The constipation self-assessment scale was utilized to analyze participants' 
949 
abdominal symptoms (including stomachache, abdominal cramping, bloating, and 
950 
discomfort), rectal symptoms (such as decreased defecation frequency, straining, pain 
951 
during defecation, incomplete evacuation, urgency with difficulty expelling, rectal 
952 
bleeding or fissures, and burning sensation), and stool hardness. In the sarcopenia 
953 
non-intervention group (SC), comparisons of pre- and post-intervention assessments 
954 
(Table 25) of abdominal and rectal symptom scores, stool hardness scores, and overall 
955 
scores indicated no statistically significant changes associated with constipation 
956 
(p>0.05). In contrast, the LcS group demonstrated significant improvements in 
957 
abdominal symptom scores and overall scores 12 weeks after Fermented Milk 
958 
Containing LcS supplementation compared to baseline (p<0.05). Nonetheless, there 
959 
were no significant changes in rectal symptom scores or stool hardness scores 
960 
post-intervention compared to pre-intervention (p>0.05). Subsequent inter-group 
961 
comparisons post-intervention between the sarcopenia non-intervention group (SC) 
962 
and the LcS group identified significant improvements in abdominal symptoms after 
963 
12 weeks of Fermented Milk Containing LcS intervention (p<0.05), whereas rectal 
964 
symptoms, stool hardness, and overall scores did not exhibit significant differences 
965 
between the groups (p>0.05). 
966 
 
967 
7.2.5 Blood biochemical parameters 
968 
To understand whether the blood biochemical profiles of the subjects could impact 
969 
subsequent experiments and to ensure that the Fermented Milk Containing LcS 
970 
intervention does not raise health concerns, this study measured blood biochemical 
971 
parameters before and after the intervention. 
972 
In the SC, pre- and post-intervention blood biochemical data (Table 26) revealed no 
973 
significant changes in TSH, Free T4, hsCRP, albumin, pre-albumin, CHOL, HDL-C, 
974 
LDL-C, TG, HbA1c, fasting glucose (GLU (AC)), insulin, HOMA-IR, AST, ALT, 
975 
BUN, creatinine, eGFR, WBC, RBC, HGB, HCT, MCHC, platelet count, and 
976 
RDW-CV (p>0.05). 
977 
Similarly, in the LcS group, pre- and post-intervention blood biochemical data 
978 

 
 
20260128 edited 
34 
 
(Table 27) showed no significant changes in TSH, Free T4, hsCRP, albumin, 
979 
pre-albumin, CHOL, HDL-C, LDL-C, TG, HbA1c, fasting glucose (GLU (AC)), 
980 
insulin, HOMA-IR, AST, ALT, BUN, creatinine, eGFR, WBC, RBC, HGB, HCT, 
981 
MCHC, platelet count, and RDW-CV (p>0.05). 
982 
  Comparing the blood biochemical data between the SC and LcS groups (Table 28), 
983 
there were no significant differences in TSH, hsCRP, albumin, pre-albumin, CHOL, 
984 
HDL-C, LDL-C, TG, HbA1c, fasting glucose (GLU (AC)), insulin, HOMA-IR, AST, 
985 
ALT, BUN, creatinine, eGFR, WBC, RBC, HGB, HCT, MCHC, platelet count, and 
986 
RDW-CV (p>0.05). However, in the LcS group, there was a significant reduction in 
987 
the number of subjects with abnormal Free T4 and fasting glucose (GLU (AC)) values 
988 
(p<0.05), and a significant increased number of subjects with abnormal LDL-C levels 
989 
(p<0.05). 
990 
 
991 
7.2.6 T cell subtypes 
992 
The results from Table 14 indicated that the proportion of CD4+ T cells is higher 
993 
in sarcopenia patients. Therefore, this study aimed to investigate whether the 
994 
Fermented Milk Containing LcS intervention could adjust the proportion of T cells in 
995 
the SC group. According to the T lymphocyte data (Table 29), the pre- and 
996 
post-intervention comparison in the SC group showed no significant differences in the 
997 
proportions of CD3+, CD4+, CD8+, and regulatory T cells (p>0.05). In the LcS group, 
998 
the pre- and post-intervention comparison also showed no significant differences in 
999 
the proportions of CD3+, CD8+, and regulatory T cells (p>0.05). However, there was 
1000 
significant reduction in the proportion of CD4+ T cells following the Fermented Milk 
1001 
Containing LcS intervention (p<0.05. When comparing the SC and LcS groups, no 
1002 
significant differences were observed in the proportions of CD3+, CD4+, CD8+, and 
1003 
regulatory T cells (p>0.05). Overall, the Fermented Milk Containing LcS intervention 
1004 
did not result in significant changes in the proportions of T lymphocytes. 
1005 
 
1006 
7.2.7 Pro-inflammatory and anti-inflammatory cytokines 
1007 
Cytokines and lower levels of anti-inflammatory cytokines compared to the normal 
1008 
group. Therefore, this study aimed to determine whether the Fermented Milk 
1009 
Containing LcS intervention could alleviate inflammation in the sarcopenia group. 
1010 
According to the data in Table 30, there were no significant differences in IL-6, IL-10, 
1011 
IL-17, and TGF-β in the pre- and post-intervention comparisons within the SC group, 
1012 

 
 
20260128 edited 
35 
 
within the LcS group, or between the SC and LcS groups. TNF-α levels showed no 
1013 
significant difference in the pre- and post-intervention comparison within the SC 
1014 
group. However, in the LcS group, the post-intervention TNF-α levels were higher 
1015 
compared to pre-intervention levels, yet significantly lower than those in the SC group. 
1016 
These results suggest that the Fermented Milk Containing LcS intervention can 
1017 
effectively reduce serum TNF-α levels. 
1018 
 
1019 
7.2.8 Antioxidant activity 
1020 
According to the data in Table 31, there were no significant differences in the 
1021 
activity of the three antioxidant enzymes in the pre- and post-intervention 
1022 
comparisons within the SC group (p>0.05). In the LcS group, there were no 
1023 
significant differences in CAT and SOD activity (p>0.05), but GPx activity showed a 
1024 
significant increase after three months of intervention (p<0.05). Comparing the SC 
1025 
and LcS groups, there were no significant differences in GPx and SOD activity 
1026 
(p>0.05), but CAT activity was significantly lower in the LcS group compared to the 
1027 
SC group (p<0.05). These results suggest that while the Fermented Milk Containing 
1028 
LcS intervention may slightly inhibit CAT activity, there were no significant changes 
1029 
in CAT within the LcS group itself. However, the intervention did lead to a significant 
1030 
increase in GPx activity. 
1031 
 
1032 
7.2.9 Anabolic hormone 
1033 
The results from Table 17 indicated that DHEA-S levels tend to decrease in 
1034 
sarcopenia patients. Therefore, this study aimed to determine whether the Fermented 
1035 
Milk Containing LcS intervention could increase DHEA-S levels in the body. 
1036 
According to the data in Table 32, there were no significant differences in DHEA-S 
1037 
levels in the pre- and post-intervention comparisons within the SC group, within the 
1038 
LcS group, or between the SC and LcS groups (p>0.05). These results suggest that the 
1039 
Fermented Milk Containing LcS intervention does not alter DHEA-S levels in the 
1040 
body. 
1041 
 
1042 
7.2.10 Metal concentration 
1043 
Research indicated that some probiotics help remove metal ions from the body. 
1044 
Therefore, this study aimed to determine if Fermented Milk Containing LcS has this 
1045 
capability. According to the data in Table 33, in the SC group, significant changes 
1046 

 
 
20260128 edited 
36 
 
were observed in Ni and Ba levels in the blood before and after the test (p<0.05). In 
1047 
the LcS group, significant decreases were observed in Na, Mg, K, Mn, Fe, Ni, Cu, Zn, 
1048 
Sr, Sb, Ba, and Pb levels before and after the test (p<0.05). When comparing the SC 
1049 
and LcS groups, the LcS group had significantly lower levels of Na, Mg, K, Mn, Fe, 
1050 
Ni, Cu, Zn, Sr, Cd, Sb, Ba, and Pb compared to the SC group (p<0.05). The results 
1051 
indicate that the intervention with Fermented Milk Containing LcS helps remove most 
1052 
metal ions from the body. 
1053 
 
1054 
7.2.11 Gut microbiota composition and microbiota-derived Metabolite Analysis 
1055 
Gut microbiota analysis revealed compositional differences between the 
1056 
sarcopenia control (SC) group and the LcS intervention group. As shown in Figures 
1057 
5A and 5B, alpha diversity indices—including the Shannon diversity index and the 
1058 
number of observed features—were slightly higher in the SC group compared to the 
1059 
LcS group; however, these differences did not reach statistical significance, 
1060 
suggesting that LcS intervention had a minimal impact on overall microbial diversity. 
1061 
Beta diversity analysis based on Bray–Curtis dissimilarity (Figure 5C) revealed a 
1062 
partial separation between the SC and LcS groups, indicating subtle differences in 
1063 
microbial community composition that were not statistically distinct. 
1064 
The taxonomic composition of the gut microbiota was evaluated at the genus 
1065 
level. Figure 5D shows average relative abundances of the 20 most abundant bacterial 
1066 
genera in both groups, while Figure 5E displays the relative abundances of the top 35 
1067 
bacterial genera in each subject.  
1068 
Differential abundant genera between the groups were identified using LEfSe 
1069 
(Figure 5F) and MegagenomeSeq (Figure 6). Notably, Bifidobacterium, Lacrimispora, 
1070 
and Lacticaseibacillus were consistently higher, while Cloacibacillus, Gehongia, 
1071 
Muribaculum, Negativibacillus, and Oscillibacter were consistently lower across both 
1072 
methods (Table 34). 
1073 
In addition to microbial composition, microbial metabolites were profiled using 
1074 
LC-MS/MS, including short-chain fatty acids (SCFAs), indole derivatives such as 
1075 
indole-3-propionic acid (IPA), and trimethylamine-N-oxide (TMAO). The results are 
1076 
presented in Table 35. Although acetic acid levels were significantly lower in the SC 
1077 
group compared to the NS group, no change was observed compared to the LcS group. 
1078 
Notably, isovaleric acid levels were significantly higher in the SC group compared to 
1079 
both the NS and LcS groups, suggesting that Fermented Milk Containing LcS 
1080 

 
 
20260128 edited 
37 
 
intervention may mitigate the elevation of isovaleric acid associated with sarcopenia.  
1081 
 
1082 
7.3 Spearman correlations between gut microbiota and specific 
1083 
sarcopenia parameter 
1084 
Based on the relative abundance in SC and LcS subjects, the top 25 bacterial 
1085 
species were selected for analysis. Spearman correlation heat maps were constructed 
1086 
to examine associations between gut microbiota and various parameters, including 
1087 
physical examinations, body composition, cytokines, DHEA-S, CD4+ T cell count, 
1088 
biochemical parameters, heavy metals, and metabolites (Figures 7-12). 
1089 
The heat map in Figure 7 presents the correlations between gut microbiota and 
1090 
physical performance metrics. Notably, Lacrimispora exhibited a negative correlation 
1091 
with the Timed Up and Go (TUG) test, while Bifidobacterium showed positive 
1092 
correlations with the Short Physical Performance Battery (SPPB) score and walking 
1093 
speed. Additionally, Klebsiella was positively correlated with the chair stand test. 
1094 
These findings suggest that Bifidobacterium, Klebsiella, and Lacrimispora may 
1095 
influence physical function in SC and LcS subjects. 
1096 
Figure 8 displays the correlations between gut microbiota and body composition 
1097 
metrics. Muribaculum was positively associated with fat-free mass, protein, and 
1098 
extracellular water. In contrast, Parabacteroides showed a negative association with 
1099 
body weight. Veillonella exhibited negative correlations with multiple parameters, 
1100 
including height, weight, skeletal muscle mass, soft lean mass, fat-free mass, protein, 
1101 
total body water, intracellular water, and extracellular water. Similarly, 
1102 
Lacticaseibacillus was negatively correlated with body weight, skeletal muscle mass 
1103 
index (SMI), skeletal muscle mass, fat-free mass, total body water, intracellular water, 
1104 
and whole-body phase angle. Conversely, Negativibacillus showed positive 
1105 
correlations with height, skeletal muscle mass, soft lean mass, fat-free mass, protein, 
1106 
total body water, intracellular water, and extracellular water. These results indicate 
1107 
that specific bacterial species may play roles in regulating body composition. 
1108 
Figure 9 highlights correlations between gut microbiota and immune-related 
1109 
parameters. Odoribacter was negatively associated with IL-10, whereas Veillonella 
1110 
showed a positive correlation with IL-10. Additionally, Lacticaseibacillus exhibited a 
1111 
negative correlation with TNF-α. These results suggest that variations in gut 
1112 
microbiota composition may influence cytokine regulation and immune responses. 
1113 
Figure 10 presents correlations between gut microbiota and biochemical 
1114 

 
 
20260128 edited 
38 
 
parameters. Odoribacter was positively associated with pre-albumin but negatively 
1115 
correlated with free-T4, high-sensitivity C-reactive protein (HS-CRP), and glutamic 
1116 
oxaloacetic transaminase (GOT). Parabacteroides was negatively associated with 
1117 
pre-albumin. Klebsiella exhibited positive correlations with thyroid-stimulating 
1118 
hormone (TSH) and blood urea nitrogen (BUN) but negative correlations with 
1119 
cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-C), and insulin. 
1120 
Bifidobacterium was positively correlated with CHOL and high-density lipoprotein 
1121 
cholesterol (HDL-C). Veillonella showed positive associations with free-T4 and 
1122 
HDL-C but a negative association with creatinine. Similarly, Lacticaseibacillus was 
1123 
positively correlated with free-T4 but negatively correlated with pre-albumin, 
1124 
creatinine, white blood cell count (WBC), and hematocrit (HCT). Negativibacillus 
1125 
was positively associated with BUN and WBC but negatively associated with free-T4, 
1126 
GOT, and mean corpuscular hemoglobin concentration (MCHC). Oscillibacter was 
1127 
positively associated with red blood cell count (RBC) but negatively correlated with 
1128 
free-T4. Gehongia was positively associated with BUN but negatively correlated with 
1129 
CHOL. Lacrimispora was positively associated with glutamic pyruvic transaminase 
1130 
(GPT) but negatively correlated with WBC and HCT. These findings suggest that 
1131 
specific gut microbiota species may play roles in lipid metabolism, insulin sensitivity, 
1132 
and hematological regulation. 
1133 
Figure 11 illustrates correlations between gut microbiota and heavy metal 
1134 
concentrations. Odoribacter showed a negative correlation with nickel (Ni), while 
1135 
Parabacteroides was negatively associated with strontium (Sr). Klebsiella exhibited 
1136 
positive correlations with Zinc (Zn), selenium (Se), and lead (Pb). Megasphaera was 
1137 
positively associated with sodium (Na), antimony (Sb), and barium (Ba). In contrast, 
1138 
Lacticaseibacillus displayed negative correlations with multiple heavy metals, 
1139 
including Na, magnesium (Mg), potassium (K), iron (Fe), Ni, Sr, cadmium (Cd), Sb, 
1140 
Ba, and Pb. Negativibacillus was positively associated with Na and K, while 
1141 
Oscillibacter showed a negative correlation with Cd. Gehongia exhibited a positive 
1142 
correlation with Na. These results indicate that gut microbiota composition may 
1143 
influence heavy metal regulation. 
1144 
Figure 12 presents correlations between gut microbiota and metabolite levels. 
1145 
Muribaculum was positively associated with isobutyric acid and valeric acid, while 
1146 
Parabacteroides showed a negative correlation with butyric acid. Klebsiella was 
1147 
positively correlated with isobutyric acid. Veillonella displayed positive associations 
1148 

 
 
20260128 edited 
39 
 
with acetic acid, propionic acid, and indolepropionic acid (IPA) but a negative 
1149 
correlation with isovaleric acid. Lacticaseibacillus was negatively associated with 
1150 
isovaleric acid. Conversely, Negativibacillus was positively correlated with isovaleric 
1151 
acid but negatively associated with acetic acid. Oscillibacter was positively associated 
1152 
with isovaleric acid but negatively correlated with acetic acid, propionic acid, and IPA. 
1153 
Gehongia was positively associated with isovaleric acid but negatively correlated with 
1154 
propionic acid. These findings suggest that the Fermented Milk Containing LcS 
1155 
intervention may significantly influence microbial-metabolite interactions. 
1156 
 
1157 
8. Discussion 
1158 
This study investigated the potential effects of fermented milk containing 
1159 
Lactobacillus casei Shirota (LcS) on sarcopenia-related biomarkers, inflammatory 
1160 
cytokines, trace metals, and physical performance. While previous research has 
1161 
demonstrated the probiotic benefits of LcS on gastrointestinal and immune function, 
1162 
its potential role in skeletal muscle health and systemic metal modulation remains 
1163 
underexplored. By comparing sarcopenia (SC), non-sarcopenia (NS), and 
1164 
LcS-intervened groups over a 12-week period, this study provides new insights into 
1165 
the multifaceted impacts of LcS supplementation in aging populations. 
1166 
Overview and Primary Endpoint 
1167 
This study investigated whether a 12-week intervention with Fermented Milk 
1168 
Containing Lactobacillus casei strain Shirota (LcS) could beneficially remodel gut 
1169 
microbiota and improve clinical parameters associated with sarcopenia in elderly 
1170 
individuals. The primary endpoint focused on microbial composition, while secondary 
1171 
endpoints included microbial metabolites, physical performance, antioxidant and 
1172 
inflammatory biomarkers, immune modulation, and heavy metal modulation. 
1173 
Although α- and β-diversity indices showed no statistically significant changes, 
1174 
genus- and species-level analyses revealed distinct compositional shifts. Both LEfSe 
1175 
and MetagenomeSeq analyses demonstrated enrichment of health-promoting 
1176 
genera—including 
Bifidobacterium, 
Lacticaseibacillus, 
and 
1177 
Lacrimispora—accompanied by a reduction in pro-inflammatory genera such as 
1178 
Muribaculum and Oscillibacter. These results indicate that LcS supplementation helps 
1179 
shift the gut microbial community toward a more homeostatic and anti-inflammatory 
1180 
state, consistent with literature describing the role of probiotics in correcting 
1181 
age-related dysbiosis. 
1182 
Increases in Bifidobacterium and Lacticaseibacillus (formerly part of the 
1183 

 
 
20260128 edited 
40 
 
Lactobacillus genus) are of particular interest. These genera have been associated 
1184 
with improved muscle mass and strength. For instance, a systematic review reported 
1185 
that higher levels of Bifidobacterium and Lactobacillus species were linked to 
1186 
increased skeletal muscle mass and function in both animal and human models of 
1187 
sarcopenia [49]. 
1188 
The observed increase in Lacrimispora abundance may also carry functional 
1189 
relevance. A recent study in mice demonstrated that certain Lacrimispora species 
1190 
improved locomotor function, possibly via modulation of host-microbiota signaling 
1191 
axes [50]. Conversely, Muribaculum has been negatively associated with physical 
1192 
performance and lean mass, appearing more frequently in frail individuals. This 
1193 
negative association was noted in the aforementioned systematic review, and further 
1194 
supported by recent findings linking Muribaculum, Negativibacillus, and 
1195 
Oscillibacter to frailty indicators in older adults [51]. The decreased abundance of 
1196 
these taxa in the LcS group may reflect a microbial shift away from profiles linked to 
1197 
sarcopenic risk. 
1198 
Currently, limited evidence exists for Cloacibacillus and Gehongia in relation to 
1199 
muscle physiology or sarcopenia. These genera remain poorly characterized. However, 
1200 
Cloacibacillus has been associated with mucosal inflammation and epithelial barrier 
1201 
disruption in some clinical contexts, which may indirectly relate to chronic low-grade 
1202 
inflammation implicated in muscle catabolism. No studies were found connecting 
1203 
Gehongia to sarcopenia or musculoskeletal outcomes, underscoring the need for 
1204 
further research into these rare genera. 
1205 
 
1206 
Microbiota-Derived Metabolites: Functional Implications 
1207 
Microbial metabolite analysis corroborated the compositional findings. While 
1208 
most short-chain fatty acids (SCFAs) remained stable, acetic acid—commonly 
1209 
reduced in sarcopenic individuals—showed an increasing trend following Fermented 
1210 
Milk Containing LcS supplementation. Notably, isovaleric acid, associated with 
1211 
proteolytic fermentation and muscle degradation, significantly decreased. These 
1212 
findings suggest that Fermented Milk Containing LcS supports a functional shift in 
1213 
microbial metabolism, favoring saccharolytic pathways associated with colonic and 
1214 
systemic health. 
1215 
 
1216 
Secondary Endpoints 
1217 

 
 
20260128 edited 
41 
 
In addition to changes in gut microbiota, this study assessed a broad range of 
1218 
secondary endpoints, including physical performance, muscle function, heavy metal, 
1219 
inflammatory cytokines, antioxidant enzyme activity, anabolic hormones, T 
1220 
lymphocyte subsets, and serum biochemical parameters. These measures were chosen 
1221 
to elucidate potential systemic effects of Fermented Milk Containing LcS beyond the 
1222 
gastrointestinal tract and explore mechanisms underlying sarcopenia progression and 
1223 
its mitigation. 
1224 
 
1225 
Physical Performance and Muscle Function 
1226 
Despite the absence of significant changes in muscle mass, the LcS group 
1227 
exhibited improved physical performance across multiple domains: chair stand time, 
1228 
walk speed, and SPPB score. These results suggest that Fermented Milk Containing 
1229 
LcS may promote neuromuscular efficiency or reduce systemic inflammation, rather 
1230 
than stimulating direct hypertrophic effects. These outcomes are clinically meaningful, 
1231 
as enhanced physical function can significantly reduce fall risk and improve quality of 
1232 
life in the elderly. 
1233 
 
1234 
Heavy Metal Modulation: A Novel Probiotic Mechanism 
1235 
A particularly novel and impactful finding was the significant reduction in 
1236 
multiple trace and heavy metals—including Ni, Sb, Ba, and Pb—exclusively in the 
1237 
LcS group. Additional decreases were noted for Na, Mg, K, Mn, Fe, Cu, Zn, Sr, and 
1238 
Cd. These reductions suggest that Fermented Milk Containing *Lactobacillus casei* 
1239 
Shirota (LcS), either directly or via microbiota restructuring, facilitates metal ion 
1240 
sequestration, transformation, or fecal elimination [52]. 
1241 
Given that chronic exposure to heavy metals contributes to oxidative stress, 
1242 
mitochondrial damage, and neuromuscular dysfunction, Fermented Milk Containing 
1243 
LcS may offer a unique probiotic-based avenue for environmental toxin mitigation in 
1244 
aging populations. Previous studies have indicated that *Lactobacillus casei* strains 
1245 
can bind and reduce gastrointestinal absorption of non-essential or toxic metals such 
1246 
as cadmium, lead, and arsenic through mechanisms like chelation and surface 
1247 
adsorption. 
1248 
It is worth noting, however, that in addition to toxic metals, reductions in 
1249 
essential elements such as magnesium (Mg) and zinc (Zn) were also observed. From a 
1250 
nutritional and clinical standpoint, both Mg and Zn are vital for muscle synthesis, 
1251 

 
 
20260128 edited 
42 
 
antioxidant defense, immune modulation, and neuromuscular function [53, 54]. Their 
1252 
decline might raise concerns about nutrient deficiency or impaired absorption. 
1253 
However, several contextual factors support a more nuanced interpretation. First, the 
1254 
post-intervention concentrations of Mg and Zn in the LcS group remained within 
1255 
clinically accepted normal reference ranges [55], and there was no evidence of 
1256 
malnutrition based on MNA scores, dietary intake, or 24-hour recall data.  
1257 
Second, rather than indicating deficiency, the observed decreases in circulating 
1258 
Zn and Mg may reflect redistribution or enhanced cellular uptake—especially in 
1259 
metabolically active tissues like muscle—potentially mediated by improved gut 
1260 
barrier integrity or systemic inflammation reduction [56]. This interpretation is 
1261 
supported by the concurrent improvements in physical performance markers such as 
1262 
walking speed, chair stand time, and SPPB scores observed in the LcS group. In this 
1263 
context, lower serum levels of Zn and Mg may indicate more efficient utilization, not 
1264 
loss. 
1265 
Third, alterations in gut microbiota composition following LcS supplementation 
1266 
may also influence micronutrient absorption and retention kinetics[57], though no 
1267 
significant differences were observed in overall micronutrient intake across study 
1268 
groups. These hypotheses warrant further investigation through future studies 
1269 
incorporating intracellular metal analysis, metalloprotein profiling, and longer 
1270 
follow-up to assess long-term micronutrient status and safety. Although our study did 
1271 
not show significant changes in skeletal muscle mass, the LcS group demonstrated 
1272 
measurable improvements in functional outcomes. These include better chair stand 
1273 
performance, increased gait speed, and higher SPPB scores—parameters closely 
1274 
linked to fall risk and frailty in older adults [58-60]. Recent literature suggests that 
1275 
probiotics may influence neuromuscular function not only by improving nutrient 
1276 
status and microbiota composition but also by lowering systemic inflammation and 
1277 
oxidative stress—key contributors to sarcopenia and motor decline [61, 62].  
1278 
Additionally, the reduction in neurotoxic metals (e.g., Pb, Cd) may contribute to 
1279 
improved neuromuscular coordination and cognitive-motor integration [63]. 
1280 
Therefore, even in the absence of measurable muscle mass gains, the overall 
1281 
functional improvements seen in the LcS group point to a clinically meaningful 
1282 
benefit of LcS as a multidimensional strategy to support older adults. Through 
1283 
anti-inflammatory, antioxidative, microbiota-modulating, and potential heavy metal 
1284 
modulation mechanisms, LcS supplementation may help preserve physical 
1285 

 
 
20260128 edited 
43 
 
independence, reduce fall risk, and enhance quality of life. 
1286 
 
1287 
Inflammatory Cytokines 
1288 
The LcS group experienced a significant reduction in TNF-α, a key 
1289 
pro-inflammatory cytokine associated with muscle catabolism and chronic 
1290 
inflammation. Other cytokines (IL-6, IL-10, IL-17, and TGF-β) did not show 
1291 
significant changes. This TNF-α decline highlights the role of gut-immune 
1292 
interactions in mediating systemic inflammatory responses and suggests that 
1293 
Fermented Milk Containing LcS exerts targeted anti-inflammatory effects via 
1294 
microbiota modulation.  
1295 
Interestingly, although most previous studies reported an increase in IL-17 in 
1296 
sarcopenic or pro-inflammatory conditions, our data demonstrated a lower IL-17 level 
1297 
in the sarcopenic group (SC) compared to the non-sarcopenic group (NS). This 
1298 
finding may be attributed to several possible explanations. 
1299 
First, it is known that IL-17 levels are influenced by metabolic conditions such as 
1300 
insulin resistance and components of metabolic syndrome. Given that our 
1301 
inclusion/exclusion criteria did not exclude participants with metabolic comorbidities, 
1302 
it is plausible that the NS group may have included individuals with elevated IL-17 
1303 
levels related to these underlying conditions. This would result in a relatively higher 
1304 
IL-17 concentration in the NS group. 
1305 
Second, because the NS group participants were recruited via the same 
1306 
sarcopenia screening pipeline, they may not represent a typical “healthy” reference 
1307 
group. The proximity in their health status to the SC group may have minimized the 
1308 
inflammatory contrast commonly seen in other case-control designs. 
1309 
These factors should be considered when interpreting the unexpected IL-17 
1310 
profile observed in our cohort, and future studies may benefit from including a 
1311 
metabolically healthy control group to clarify this observation. 
1312 
 
1313 
Antioxidant Enzyme Activity 
1314 
Among the antioxidant enzymes measured, only catalase (CAT) activity showed 
1315 
a significant difference between groups, with lower CAT activity observed in the LcS 
1316 
group compared to the SC group. This between-group difference may reflect a 
1317 
reduced oxidative burden, potentially mediated by changes in microbiota composition 
1318 
or reduced exposure to pro-oxidant stimuli such as heavy metals. Other enzymes, 
1319 

 
 
20260128 edited 
44 
 
including glutathione peroxidase (GPx), did not show significant intergroup 
1320 
differences and are therefore not further interpreted in this context. 
1321 
 
1322 
Immune Modulation 
1323 
Flow cytometry revealed a trend toward reduced CD4+ T cell proportions within 
1324 
the LcS group (p < 0.05). Although not statistically significant and with no difference 
1325 
observed between groups, this trend aligns with prior observations that elevated CD4+ 
1326 
T cells are linked to sarcopenia. Regulatory T cells (Tregs) remained stable; however, 
1327 
their activity could be involved in maintaining mucosal tolerance and systemic 
1328 
immune balance. Overall, these changes support the potential immunomodulatory role 
1329 
of Fermented Milk Containing LcS through gut-mediated signaling. 
1330 
 
1331 
Anabolic Hormones and Nutritional Indices 
1332 
Serum levels of DHEA-S, a hormone associated with muscle integrity and aging, 
1333 
remained unchanged, suggesting that Fermented Milk Containing LcS-driven 
1334 
improvements in function are unlikely to be hormone-mediated. Nutritional status 
1335 
assessed by the Mini Nutritional Assessment (MNA) improved post-intervention, 
1336 
indicating enhanced nutritional resilience. Notably, gastrointestinal symptoms related 
1337 
to constipation significantly improved in the LcS group, which may be partially 
1338 
attributable to microbiota restructuring; however, this effect is unlikely to be mediated 
1339 
by a global increase in SCFA production, as only selected metabolites showed 
1340 
significant changes (e.g., decreased isovaleric acid). 
1341 
Although the total daily caloric intake in both groups appears lower than WHO 
1342 
general recommendations (2,000–3,000 kcal for men and 1,600–2,400 kcal for 
1343 
women), this finding is consistent with national data from the Taiwanese older adult 
1344 
population. According to the Nutrition and Health Survey in Taiwan (NAHSIT 2013–
1345 
2016), the average energy intake for men and women aged 65 and above is 1,817 kcal 
1346 
and 1,399 kcal, respectively, which closely aligns with our results. 
1347 
This trend may be attributed to multiple factors including age-related reductions 
1348 
in basal metabolic rate and appetite, cultural dietary patterns emphasizing plant-based 
1349 
and low-fat meals, and the relatively low physical activity level of participants as 
1350 
shown in IPAQ assessments. Notably, most participants were women, further 
1351 
lowering the overall caloric average. 
1352 
Importantly, no participant met the criteria for clinical malnutrition as assessed 
1353 

 
 
20260128 edited 
45 
 
by the Mini Nutritional Assessment (MNA) or 24-hour dietary recalls. Therefore, the 
1354 
lower energy intake observed should be interpreted as physiologically and culturally 
1355 
normative rather than indicative of nutritional insufficiency. 
1356 
 
1357 
Serum Biochemical Safety Profile 
1358 
Comprehensive serum biochemical profiles—including lipid panels, liver and 
1359 
kidney function, glucose metabolism, and hematologic markers—remained stable and 
1360 
within reference ranges post-intervention. These data confirm that Fermented Milk 
1361 
Containing LcS supplementation is safe and well-tolerated in elderly individuals with 
1362 
sarcopenia. 
1363 
 
1364 
Integrated Mechanistic Interpretation 
1365 
The integrated findings from microbial, metabolic, immune, and functional 
1366 
endpoints suggest that Fermented Milk Containing LcS acts through multi-layered 
1367 
biological mechanisms, including: 
1368 
 
Microbiota remodeling toward eubiotic, anti-inflammatory profiles   
1369 
 
Metabolite modulation promoting beneficial SCFAs and reducing toxic 
1370 
fermentation products   
1371 
 
Improved physical function independent of muscle hypertrophy. 
1372 
 
Enhancing heavy metal modulation, potentially indicating a reduction in 
1373 
oxidative stress burden, as supported by deceased CAT activity. 
1374 
 
Reduction in TNF-α, indicating systemic anti-inflammatory effects 
1375 
This constellation of effects points to Fermented Milk Containing LcS as a 
1376 
metabolic and immunological modulator with potential to reduce sarcopenia risk and 
1377 
promote healthy aging. 
1378 
 
1379 
Translational Implications and Future Directions 
1380 
These results position Fermented Milk Containing LcS as a promising low-risk, 
1381 
microbiota-targeted intervention with broad applicability in geriatric care. Beyond 
1382 
sarcopenia, the observed heavy metal modulation and anti-inflammatory effects may 
1383 
benefit individuals exposed to environmental toxins or those at risk of metabolic and 
1384 
degenerative disorders. 
1385 
Future studies should explore: 
1386 

 
 
20260128 edited 
46 
 
 
Functional metagenomics to identify microbial genes responsible for metal 
1387 
metabolism   
1388 
 
Mechanistic studies on the gut-muscle axis   
1389 
 
Long-term trials assessing durability of benefits and impact on clinical outcomes 
1390 
such as fall risk and hospitalization 
1391 
 
1392 
Limitations 
1393 
One limitation of this study is its open-label design, which may introduce various 
1394 
biases, particularly in subjective outcomes; to address this, we contextualized our 
1395 
findings by evaluating objective measures, including serum biomarkers and gut 
1396 
microbiota. Additionally, the NS group may have been approaching frailty thresholds, 
1397 
despite recruitment of non-sarcopenic older adults based on the Asian Working Group 
1398 
for Sarcopenia (AWGS) criteria, which focus primarily on muscle-related indicators. 
1399 
This is suggested by the median usual walking speed in the NS group, which fell 
1400 
below 1.0 m/s, as well as by comparisons with broader sarcopenia criteria proposed 
1401 
by Fried et al. and the Japanese CHS adaptation [47,48]. These observations 
1402 
underscore the complexity of functional decline in aging and highlight the need for 
1403 
multi-dimensional screening strategies that extend beyond muscle mass alone in 
1404 
future studies. 
1405 
 
1406 
Conclusion 
1407 
Fermented Milk Containing LcS supplementation over 12 weeks in elderly 
1408 
individuals with sarcopenia led to: 
1409 
 
Remodeling of gut microbiota composition 
1410 
 
Functional metabolic improvements 
1411 
 
Enhanced physical performance 
1412 
 
Reduction of pro-inflammatory markers 
1413 
 
Modulation of environmental heavy metals 
1414 
 
Reduced oxidative stress burden 
1415 
Together, these findings support the therapeutic potential of Fermented Milk 
1416 
Containing LcS as a multi-targeted strategy for sarcopenia management and healthy 
1417 
aging. 
1418 

 
 
20260128 edited 
47 
 
References 
1419 
1. 
Morley, J.E., S.D. Anker, and S. von Haehling, Prevalence, incidence, and 
1420 
clinical impact of sarcopenia: facts, numbers, and epidemiology-update 2014. 
1421 
J Cachexia Sarcopenia Muscle, 2014. 5(4): p. 253-9. 
1422 
2. 
Bianchi, L., et al., Prevalence and Clinical Correlates of Sarcopenia, 
1423 
Identified According to the EWGSOP Definition and Diagnostic Algorithm, in 
1424 
Hospitalized Older People: The GLISTEN Study. J Gerontol A Biol Sci Med 
1425 
Sci, 2017. 72(11): p. 1575-1581. 
1426 
3. 
Marzetti, E., et al., Sarcopenia: an overview. Aging Clin Exp Res, 2017. 29(1): 
1427 
p. 11-17. 
1428 
4. 
Newman, A.B., et al., Strength and muscle quality in a well-functioning cohort 
1429 
of older adults: the Health, Aging and Body Composition Study. J Am Geriatr 
1430 
Soc, 2003. 51(3): p. 323-30. 
1431 
5. 
Visser, M., et al., Relationship of interleukin-6 and tumor necrosis 
1432 
factor-alpha with muscle mass and muscle strength in elderly men and women: 
1433 
the Health ABC Study. J Gerontol A Biol Sci Med Sci, 2002. 57(5): p. 
1434 
M326-32. 
1435 
6. 
Miller, M.S., et al., Age-related slowing of myosin actin cross-bridge kinetics 
1436 
is sex specific and predicts decrements in whole skeletal muscle performance 
1437 
in humans. J Appl Physiol (1985), 2013. 115(7): p. 1004-14. 
1438 
7. 
Miller, M.S., D.M. Callahan, and M.J. Toth, Skeletal muscle myofilament 
1439 
adaptations to aging, disease, and disuse and their effects on whole muscle 
1440 
performance in older adult humans. Front Physiol, 2014. 5: p. 369. 
1441 
8. 
Bian, A.L., et al., A study on relationship between elderly sarcopenia and 
1442 
inflammatory factors IL-6 and TNF-alpha. Eur J Med Res, 2017. 22(1): p. 25. 
1443 
9. 
Harman, D., Aging: a theory based on free radical and radiation chemistry. J 
1444 
Gerontol, 1956. 11(3): p. 298-300. 
1445 
10. 
Nacarelli, T., A. Azar, and C. Sell, Aberrant mTOR activation in senescence 
1446 
and aging: A mitochondrial stress response? Exp Gerontol, 2015. 68: p. 
1447 
66-70. 
1448 
11. 
Deane, C.S., et al., "Nutraceuticals" in relation to human skeletal muscle and 
1449 
exercise. Am J Physiol Endocrinol Metab, 2017. 312(4): p. E282-E299. 
1450 
12. 
Gomez-Cabrera, M.C., et al., Oral administration of vitamin C decreases 
1451 
muscle mitochondrial biogenesis and hampers training-induced adaptations in 
1452 
endurance performance. Am J Clin Nutr, 2008. 87(1): p. 142-9. 
1453 
13. 
Glover, E.I., et al., Little change in markers of protein breakdown and 
1454 
oxidative stress in humans in immobilization-induced skeletal muscle atrophy. 
1455 
Appl Physiol Nutr Metab, 2010. 35(2): p. 125-33. 
1456 
14. 
Montiel-Castro, A.J., et al., The microbiota-gut-brain axis: neurobehavioral 
1457 
correlates, health and sociality. Front Integr Neurosci, 2013. 7: p. 70. 
1458 
15. 
Cho, I. and M.J. Blaser, The human microbiome: at the interface of health and 
1459 
disease. Nat Rev Genet, 2012. 13(4): p. 260-70. 
1460 
16. 
Heintz, C. and W. Mair, You are what you host: microbiome modulation of the 
1461 
aging process. Cell, 2014. 156(3): p. 408-11. 
1462 
17. 
Claesson, M.J., et al., Gut microbiota composition correlates with diet and 
1463 
health in the elderly. Nature, 2012. 488(7410): p. 178-84. 
1464 
18. 
Yatsunenko, T., et al., Human gut microbiome viewed across age and 
1465 
geography. Nature, 2012. 486(7402): p. 222-7. 
1466 
19. 
Sharon, G., et al., The Central Nervous System and the Gut Microbiome. Cell, 
1467 
2016. 167(4): p. 915-932. 
1468 

 
 
20260128 edited 
48 
 
20. 
Langille, M.G., et al., Microbial shifts in the aging mouse gut. Microbiome, 
1469 
2014. 2(1): p. 50. 
1470 
21. 
Magrone, T. and E. Jirillo, The interaction between gut microbiota and 
1471 
age-related changes in immune function and inflammation. Immun Ageing, 
1472 
2013. 10(1): p. 31. 
1473 
22. 
Thevaranjan, N., et al., Age-Associated Microbial Dysbiosis Promotes 
1474 
Intestinal Permeability, Systemic Inflammation, and Macrophage Dysfunction. 
1475 
Cell Host Microbe, 2017. 21(4): p. 455-466 e4. 
1476 
23. 
Cevenini, E., D. Monti, and C. Franceschi, Inflamm-ageing. Curr Opin Clin 
1477 
Nutr Metab Care, 2013. 16(1): p. 14-20. 
1478 
24. 
Franceschi, C., et al., Immunosenescence in humans: deterioration or 
1479 
remodelling? Int Rev Immunol, 1995. 12(1): p. 57-74. 
1480 
25. 
Franceschi, C., et al., Inflammaging and anti-inflammaging: a systemic 
1481 
perspective on aging and longevity emerged from studies in humans. Mech 
1482 
Ageing Dev, 2007. 128(1): p. 92-105. 
1483 
26. 
O'Mahony, S.M., et al., Serotonin, tryptophan metabolism and the 
1484 
brain-gut-microbiome axis. Behav Brain Res, 2015. 277: p. 32-48. 
1485 
27. 
Yano, J.M., et al., Indigenous bacteria from the gut microbiota regulate host 
1486 
serotonin biosynthesis. Cell, 2015. 161(2): p. 264-76. 
1487 
28. 
Simpson, H.L. and B.J. Campbell, Review article: dietary fibre-microbiota 
1488 
interactions. Aliment Pharmacol Ther, 2015. 42(2): p. 158-79. 
1489 
29. 
Conterno, L., et al., Obesity and the gut microbiota: does up-regulating 
1490 
colonic fermentation protect against obesity and metabolic disease? Genes 
1491 
Nutr, 2011. 6(3): p. 241-60. 
1492 
30. 
Rios-Covian, D., et al., Intestinal Short Chain Fatty Acids and their Link with 
1493 
Diet and Human Health. Front Microbiol, 2016. 7: p. 185. 
1494 
31. 
Pike, J. and R.K. Chandra, Effect of vitamin and trace element 
1495 
supplementation on immune indices in healthy elderly. Int J Vitam Nutr Res, 
1496 
1995. 65(2): p. 117-21. 
1497 
32. 
Nogawa, K. and T. Kido, Biological monitoring of cadmium exposure in 
1498 
itai-itai disease epidemiology. Int Arch Occup Environ Health, 1993. 65(1 
1499 
Suppl): p. S43-6. 
1500 
33. 
Harada, M., Minamata disease: methylmercury poisoning in Japan caused by 
1501 
environmental pollution. Crit Rev Toxicol, 1995. 25(1): p. 1-24. 
1502 
34. 
Vig, E.K. and H. Hu, Lead toxicity in older adults. J Am Geriatr Soc, 2000. 
1503 
48(11): p. 1501-6. 
1504 
35. 
Yoo, J.I., et al., High Levels of Heavy Metals Increase the Prevalence of 
1505 
Sarcopenia in the Elderly Population. J Bone Metab, 2016. 23(2): p. 101-9. 
1506 
36. 
Hanna, B.S., et al., The gut microbiota promotes distal tissue regeneration via 
1507 
RORgamma(+) regulatory T cell emissaries. Immunity, 2023. 56(4): p. 
1508 
829-846 e8. 
1509 
37. 
Nelke, C., et al., Skeletal muscle as potential central link between sarcopenia 
1510 
and immune senescence. EBioMedicine, 2019. 49: p. 381-388. 
1511 
38. 
Miyazaki, K. and T. Matsuzaki, Health properties of milk fermented with 
1512 
Lactobacillus casei strain shirota (LcS). 2008. p. 165-208. 
1513 
39. 
Benton, D., C. Williams, and A. Brown, Impact of consuming a milk drink 
1514 
containing a probiotic on mood and cognition. Eur J Clin Nutr, 2007. 61(3): p. 
1515 
355-61. 
1516 
40. 
Rao, A.V., et al., A randomized, double-blind, placebo-controlled pilot study 
1517 
of a probiotic in emotional symptoms of chronic fatigue syndrome. Gut Pathog, 
1518 

 
 
20260128 edited 
49 
 
2009. 1(1): p. 6. 
1519 
41. 
Kato-Kataoka, A., et al., Fermented milk containing Lactobacillus casei strain 
1520 
Shirota prevents the onset of physical symptoms in medical students under 
1521 
academic examination stress. Benef Microbes, 2016. 7(2): p. 153-6. 
1522 
42. 
Chen, L.H., et al., Probiotic supplementation attenuates age-related 
1523 
sarcopenia via the gut-muscle axis in SAMP8 mice. J Cachexia Sarcopenia 
1524 
Muscle, 2022. 13(1): p. 515-531. 
1525 
43. 
Wang, Y.F., et al., Endothelium-Derived 5-Methoxytryptophan Is a 
1526 
Circulating Anti-Inflammatory Molecule That Blocks Systemic Inflammation. 
1527 
Circ Res, 2016. 119(2): p. 222-36. 
1528 
44. 
Wu, J.Y., et al., Cancer-Derived Succinate Promotes Macrophage 
1529 
Polarization and Cancer Metastasis via Succinate Receptor. Mol Cell, 2020. 
1530 
77(2): p. 213-227 e5. 
1531 
45. 
Dumke, B.R. and S.J. Lees, Age-related impairment of T cell-induced skeletal 
1532 
muscle precursor cell function. Am J Physiol Cell Physiol, 2011. 300(6): p. 
1533 
C1226-33. 
1534 
46. 
Nascimento, L.C.P.d., et al., Sarcopenia and consumptive syndrome in 
1535 
HIV-infected patients receiving antiretroviral therapy in a public hospital in 
1536 
Northeast Brazil. Revista chilena de nutrición, 2020. 47(3): p. 430-442. 
1537 
47. 
Fried, L.P., et al., Frailty in older adults: evidence for a phenotype. J Gerontol 
1538 
A Biol Sci Med Sci, 2001. 56(3): p. M146-56. 
1539 
48. 
Komiya, H., et al., Validation of the Japanese version of the Clinical Frailty 
1540 
Scale. Geriatr Gerontol Int, 2025. 25(3): p. 411-417. 
1541 
49. 
Liu, C., et al., Understanding the gut microbiota and sarcopenia: a systematic 
1542 
review. J Cachexia Sarcopenia Muscle, 2021. 12(6): p. 1393-1407. 
1543 
50. 
Ahn, J.S., et al., Identification of the Intestinal Microbes Associated with 
1544 
Locomotion. Int J Mol Sci, 2023. 24(14). 
1545 
51. 
Le Cosquer, G., et al., Pathogenicity of commensal gut biofilm in prefrail 
1546 
aging. NPJ Biofilms Microbiomes, 2025. 11(1): p. 84. 
1547 
52. 
Krajewski, K., Heavy metals, noradrenaline/adrenaline ratio, and 
1548 
microbiome-associated hormone precursor metabolites: biomarkers for social 
1549 
behaviour, ADHD symptoms, and executive function in children. Sci Rep, 
1550 
2025. 15(1): p. 19006. 
1551 
53. 
Cruz, K.J., A.R. de Oliveira, and N. Marreiro Ddo, Antioxidant role of zinc in 
1552 
diabetes mellitus. World J Diabetes, 2015. 6(2): p. 333-7. 
1553 
54. 
Grober, U., J. Schmidt, and K. Kisters, Magnesium in Prevention and Therapy. 
1554 
Nutrients, 2015. 7(9): p. 8199-226. 
1555 
55. 
Dietary Reference Intakes for Calcium, Phosphorus, Magnesium, Vitamin D, 
1556 
and Fluoride, in Dietary Reference Intakes for Calcium, Phosphorus, 
1557 
Magnesium, Vitamin D, and Fluoride. 1997: Washington (DC). 
1558 
56. 
Ferenc, K., et al., Modulation of the Gut Microbiota by Nutrition and Its 
1559 
Relationship to Epigenetics. Int J Mol Sci, 2024. 25(2). 
1560 
57. 
Zhai, Q., A. Narbad, and W. Chen, Dietary strategies for the treatment of 
1561 
cadmium and lead toxicity. Nutrients, 2015. 7(1): p. 552-71. 
1562 
58. 
Rolland, Y., et al., Physical performance measures as predictors of mortality 
1563 
in a cohort of community-dwelling older French women. Eur J Epidemiol, 
1564 
2006. 21(2): p. 113-22. 
1565 
59. 
Studenski, S., et al., Gait speed and survival in older adults. JAMA, 2011. 
1566 
305(1): p. 50-8. 
1567 
60. 
Beck Jepsen, D., et al., Predicting falls in older adults: an umbrella review of 
1568 

 
 
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instruments assessing gait, balance, and functional mobility. BMC Geriatr, 
1569 
2022. 22(1): p. 615. 
1570 
61. 
Ticinesi, A., et al., Gut Microbiota, Muscle Mass and Function in Aging: A 
1571 
Focus on Physical Frailty and Sarcopenia. Nutrients, 2019. 11(7). 
1572 
62. 
Suvorov, A., Gut microbiota, probiotics, and human health. Biosci Microbiota 
1573 
Food Health, 2013. 32(3): p. 81-91. 
1574 
63. 
Sanders, A.P., B. Claus Henn, and R.O. Wright, Perinatal and Childhood 
1575 
Exposure to Cadmium, Manganese, and Metal Mixtures and Effects on 
1576 
Cognition and Behavior: A Review of Recent Literature. Curr Environ Health 
1577 
Rep, 2015. 2(3): p. 284-94. 
1578 
 
 
1579 

 
 
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Figure 2. Participant flowchart of patients eligible, recruited, numbers followed 
up and included in analysis. 
 
 

 
 
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Table 3. Baseline characteristics for the PP population 
Characteristic 
NS (N=40) a 
SC (N=40) a 
LcS (N=40) a 
Age (year) 
72.56±4.33 
73.88±5.66 
73.77±5.70 
Gender (Male/Female) 
12/28 
10/30 
5/35 
Height (cm) 
156.91±7.37 
154.84±6.68 
153.46±6.98 
Weight (kg) 
60.56±8.35 
49.61±5.81 
48.86±6.40 
BMI (kg/m2) 
24.57±2.65 
20.75±2.49 
20.73±2.28 
SMI (kg/m2) 
6.70±1.00 
5.49±0.65 
5.28±0.58 
Skeletal Muscle Mass (kg) 
22.47±4.80 
18.17±2.60 
17.42±2.64 
Fat (kg) 
18.93±6.07 
14.95±4.29 
15.65±4.42 
Total Body Water (kg) 
30.59±6.32 
25.33±3.34 
24.40±3.26 
SARC-F (score) 
1.00±1.36 
1.00±1.23 
1.00±1.79 
Calf circumference (cm) 
35.25±3.06 
32.00±2.27 
31.50±2.07 
Handgrip (kg) 
22.17±7.46 
17.77±5.70 
17.83±5.49 
Chair stand test (second) 
12.91±5.05 
14.39±10.59 
13.90±4.63 
Walk speed (m/s) 
0.89±0.21 
0.83±0.36 
0.86±0.30 
SPPB score (pt) 
11.00±1.82 
10.00±2.04 
10.00±2.07 
TUG test (second) 
10.12±3.18 
11.05±5.11 
11.64±3.02 
Albumin (g/dL) 
4.45±0.30 
4.40±0.23 
4.50±0.41 
GLU(AC) (mg/dL) 
98.00±22.10 
99.50±23.79 
93.00±22.94 
a Data were presented as mean±SD 
 
 

 
 
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Sarcopenia vs healthy subjects: 
Table 4. Descriptive statistics of basic information on demographics of subjects. 
Variable 
NS (n=40) 
SC (n=80) 
p-value 
Age, median (IQR) 
73 (5.00) 
74 (8.75) 
0.408 a 
Gender, n (%) 
 
 
 
Female 
28 (70.0) 
65 (81.25) 
0.164 b 
Male 
12 (30.0) 
15 (18.75) 
Education levels, n (%) 
 
 
 
Graduate school 
4 (10.00) 
5 (6.25)  
0.330 b 
College (or junior college) 
19 (47.50) 
24 (30.00) 
Senior (Vocational) high 
school 
10 (25.00) 
25 (31.25) 
Junior high school 
2 (5.00) 
10 (12.50) 
Elementary school 
5 (12.50) 
15 (18.75) 
No education 
0 (0.00) 
1 (1.25) 
Annual household income, n (%) 
 
 
 
Above 2 millions 
0 (0.00) 
1 (1.25) 
0.186 c 
1.5~2 millions 
0 (0.00) 
1 (1.25) 
1.0~1.5 millions 
1 (2.50) 
1 (1.25) 
0.5~1.0 million 
7 (17.50) 
4 (5.00) 
Less than 0.5 million 
32 (80.00) 
73 (91.25) 
Medical history, n (%) 
 
 
 
Yes 
29 (72.50) 
50 (62.50) 
0.276 b 
No 
11 (27.50) 
30 (37.50) 
a Statistical results were determined by using Mann Whitney test 
b Statistical results were determined by using Chi square (X2) test 
c Statistical results were determined by using Fisher exact test 
The medical history were listed in the appendix II 
 
 
 
 

 
 
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Table 5. Descriptive statistics of lifestyle on demographic characteristics of subjects 
Variable 
NS (n=40) a 
SC (n=80) a 
p-value 
Daily personal habits 
 
 
 
Drinking coffee, n (%) 
 
 
 
Yes 
23 (57.50) 
34 (42.50) 
0.121 b 
No 
17 (42.50) 
46 (57.50) 
Drinking tea, n (%) 
 
 
 
Yes 
16 (40.00) 
21 (26.25) 
0.124 b 
No 
24 (60.00) 
59 (73.75) 
Drinking alcohol, n (%) 
 
 
 
Yes 
 5 (12.50) 
 1(1.25) 
0.015 *c  
No 
35 (87.50) 
79 (98.75) 
Vitamin supplement, n (%) 
 
 
 
Yes 
32 (80.00) 
57 (71.25) 
0.302 b 
No 
8 (20.00) 
23 (28.75) 
Oil-smoke exposure assessment 
 
 
 
Oil fume exposure every day, n (%) 
 
 
 
Yes 
27 (67.50) 
49 (61.25) 
0.503 b 
No 
13 (32.50) 
31 (38.75) 
Utilizing a range hood at home, n (%) 
 
 
 
Yes 
 
 
0.106 b 
a. Always utilized (≧3 times/week) 
33 (82.50) 
59 (73.75) 
b. Rarely utilized (≦3 times/week) 
3 (7.50) 
2 (2.50) 
No 
4 (10.00) 
19 (23.75) 
Utilizing incense at home, n (%) 
 
 
 
Yes 
 
 
0.576 b 
a. ≦ 1 time/month 
0 (0.00) 
3 (3.75) 
b. ≧ 1 time/month -≦1 time/week 
1 (2.50) 
2 (2.50) 
c. ≧ 1 time/week-≦1 time/day 
0 (0.00) 
2 (2.50) 
d. ≧ 1 time/day 
5 (12.50) 
7 (8.75) 
No 
34 (85.00) 
66 (82.50) 
Utilizing mosquito coil at home, n (%) 
 
 
 
Yes 
 
 
0.028* b 
a. ≦ 1 time/month 
2 (5.00) 
0 (0.00) 

 
 
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b. ≧ 1 time/month -≦1 time/week 
0 (0.00) 
1 (1.25) 
c. ≧ 1 time/week-≦1 time/day 
1 (2.50) 
0 (0.00) 
d. ≧ 1 time/day 
2 (5.00) 
0 (0.00) 
No 
35 (87.50) 
79 (98.75) 
Cigarette smoking habit, n (%) 
 
 
 
Yes 
1 (2.50) 
2 (2.50) 
1.000c 
No 
39 (97.50) 
78 (97.50) 
Secondhand smoke exposure, n (%) 
 
 
 
Yes 
0 (0.00) 
8 (10.00) 
0.051c 
No 
40 (100.00) 
72 (90.00) 
a Data are presented as N (%) 
b Statistical results were determined by using Pearson chi square (X2) test 
c Statistical results were determined by using Fisher exact test 
*p<0.05 
 
 
 

 
 
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Table 6. Comparison of body composition between the NS and SC groups  
Parameter 
NS (n=40) a 
SC (n=80) a 
p-value b 
Body composition 
 
 
Calf circumference (cm) 
35.25 (3.00) 
31.55 (2.43) 
0.000*** 
Body height (cm) 
156 (8.8) 
154 (9.0) 
0.064# 
Body weight (kg) 
58.65 (13.58) 
49.00 (8.00) 
0.000*** 
BMI (kg/m2) 
24.23 (2.93) 
20.80 (2.96) 
0.000*** 
SMI (kg/m2) 
6.35 (0.90) 
5.4 (0.58) 
0.000*** 
Skeletal muscle mass (kg) 
20.95 (4.28) 
17.60 (2.62) 
0.000*** 
Soft lean mass (kg) 
36.2 (6.75) 
31.4 (3.88) 
0.000*** 
Fat free mass (kg) 
38.55 (7.02) 
33.35 (4.50) 
0.000*** 
Protein (kg) 
7.55 (1.40) 
6.50 (0.98) 
0.000*** 
Fat (kg) 
18.80 (5.93) 
15.30 (6.10) 
0.000*** 
Intracellular water (kg) 
17.60 (3.27) 
15.00 (2.02) 
0.000*** 
Extracellular water (kg) 
11.10 (1.85) 
9.50 (1.18) 
0.000*** 
Total body water (kg) 
28.35 (5.17) 
24.50 (2.97) 
0.000*** 
Whole body phase angle (∘) 
5.20 (1.18) 
4.60 (0.58) 
0.000*** 
a Data were presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test; # p<0.1, *p<0.05, 
**p<0.01, ***p<0.001 
 

 
 
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Table 7. Comparison of physical performance between the NS and SC groups  
Parameter 
NS (n=40) a 
SC (n=80) a 
p-value b 
Physical performance 
 
 
Handgrip (kg) 
22.17 (9.31) 
17.84 (7.02) 
0.000*** 
Chair stand test (sec) 
12.91 (5.51) 
14.02 (5.37) 
0.149 
Walk speed (m/s) 
0.893 (0.307) 
0.843 (0.324) 
0.412 
TUG test (sec) 
10.12 (3.90) 
11.38 (4.09) 
0.152 
SPPB score (pt) 
11 (3.75) 
10 (3.00) 
0.005** 
a Data were presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test; # p<0.1, *p<0.05, 
**p<0.01, ***p<0.001 
 
Table 8. Comparison of physical activity by IPAQ between NS and SC groups 
Physical 
activity 
NS group (n=40) a 
SC group (n=80) a 
Chi-square (χ
2) 
p-value b 
High 
14 (35.00) 
24 (30.00) 
1.887 
0.389 
Medium 
17 (42.50) 
44 (55.00) 
Low 
9 (22.50) 
12 (15.00) 
a Data are presented as N (%) 
b p-value was determined by using Pearson chi square (X2) test  
 
 

 
 
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Table 9. Comparison of nutritional assessment by MNA score between NS and SC 
groups  
MNA score 
NS group (n=40) a 
SC group (n=80) a 
Chi-square (χ
2) p-value b 
Well-nourished 
40 (100.00) 
66 (82.50) 
7.925 
0.002** 
At risk of 
malnourished 
0 (0.00) 
14 (17.50) 
Malnourished 
0 (0.00) 
0 (0.00) 
a Data are presented as N (%) 
b p-value was determined by using Fisher’s exact test (two-tailed) 
*p<0.05, **p<0.01. 
 
Table 10. Comparison of nutritional assessment by 24 hour-recall methods between 
NS and SC groups 
24 hour-recall methods 
NS group (n=40) a  SC group (n=80) a  p-value b 
Total calories (kcal) 
1260 (399) 
1251 (449) 
0.717 
Carbohydrate (%) 
50.76 (14.80) 
47.63 (14.10) 
0.758 
Protein (%) 
18.59 (5.26) 
17.03 (6.10) 
0.202 
Fat (%) 
31.96 (13.37) 
33.97 (12.19) 
0.496 
a Data are presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test; *p<0.05, **p<0.01, 
***p<0.001 
 
 
 

 
 
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Table 11. Descriptive statistics of Food frequency ingestion on demographic 
characteristics of subjects 
Variable 
NS (n=40) a 
SC (n=80) a 
p-value b 
Food frequency ingestion 
 
 
 
Fish ingestion 
 
 
 
The frequency of fish is ingested 
(times/month)  
8.00 (14.00) 
6.00 (12.38) 
0.270 
The frequency with which deep 
fish is ingested (times/month)  
4.00 (6.88) 
4.00(8.00) 
0.652 
The frequency at which other 
seafood is ingested 
(times/month)  
5.50 (12.26) 
3.00 (6.00) 
0.019*  
Meat ingestion 
 
 
 
The frequency with which meat 
is ingested (times/month)  
28.50 (31.75) 
23.50 (24.13) 
0.151 
Animal source of viscous of blood 
ingestion  
 
 
 
The frequency with which animal 
source of viscera or blood is 
ingested (times/month)  
0.00 (1.00) 
0.00 (0.00) 
0.016* 
Meat processing product 
 
 
 
The frequency with which meat 
processing products are ingested 
(times/month)  
2.00 (4.38) 
1.00 (4.75) 
0.032*  
Soybean ingestion 
 
 
 
The frequency with which 
soybean products are ingested 
(times/month)  
9.25 (12.75) 
9.50 (15.50) 
0.825 
a Data are presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test 
*p<0.05 
 
 
 

 
 
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Table 12. Comparison severity of constipation symptoms by PAC-SYM between the 
NS and SC groups 
PAC-SYM 
NS group (n=40) a SC group (n=80) a p-value b 
Abdominal 
0 (1) 
0 (1) 
0.290 
Rectal 
0 (1.75) 
0 (1) 
0.920 
Stools 
0 (1) 
0 (0) 
0.155 
Total 
1 (4.75) 
1 (3) 
0.454 
a Data are presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test; *p<0.05, **p<0.01, 
***p<0.001 
 
 
 

 
 
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Table 13. Comparison of biochemical parameters between NS and SC groups 
Biochemical parameter 
NS group (n=40) a  
SC group (n=80) a  
Chi-square 
(χ
2) 
p-Value 
Normal 
Abnormal 
Normal 
Abnormal 
TSH 
39 (97.50) 
1 (2.50) 
77 (96.25) 
3 (3.75) 
0.129 
1.000 
c 
Free T4 
38 (95.00) 
2 (5.00) 
72 (90.00) 
8 (10.00) 
0.873 
0.493
 c 
hsCRP (mg/dL) 
40 (100.00) 
0 (0.00) 
79 (98.75) 
1 (1.25) 
0.504 
1.000 
c 
Albumin (g/dL) 
40 (100.00) 
0 (0.00) 
78 (97.50) 
2 (2.50) 
1.017 
0.552 
c 
Pre-albumin (mg/dL) 
38 (95.00) 
2 (5.00) 
78 (97.50) 
2 (2.50) 
0.104 
1.000 
c 
CHOL (mg/dL) 
32 (80.00) 
8 (20.00) 
47 (58.75) 
33 (41.25) 
1.440 
0.230 
b 
HDL-C (mg/dL) 
30 (75.00) 
10 (25.00) 
69 (86.25) 
11 (13.75) 
2.338 
0.126 
b 
LDL-C (mg/dL) 
22 (55.00) 
18 (45.00) 
35 (43.75) 
45 (56.25) 
1.353 
0.245 
b 
TG (mg/dL) 
33 (82.50) 
7 (17.50) 
70 (87.50) 
10 (12.50) 
0.548 
0.459
 b 
HbA1c (%) 
14 (35.00) 
26 (65.00) 
34 (42.50) 
46 (57.50) 
0.625 
0.429 
b 
GLU(AC) (mg/dL) 
23 (57.50) 
17 (42.50) 
47 (58.75) 
33 (41.25) 
0.017 
0.896 
b 
Insulin (uIU/mL) 
38 (95.00) 
2 (5.00) 
79 (98.75) 
1 (1.25) 
1.538 
0.257 
c 
HOMA-IR 
16 (40.00) 
24 (60.00) 
37 (46.25) 
43 (53.75) 
0.422 
0.516 
b 
AST (U/L) 
40 (100.00) 
0 (0.00) 
77 (96.25) 
3 (3.75) 
1.538 
0.550 
c 
ALT (U/L) 
40 (100.00) 
0 (0.00) 
77 (96.25) 
3 (3.75) 
1.538 
0.550
 c 
BUN (mg/dL) 
34 (85.00) 
6 (15.00) 
65 (81.25) 
15 (18.75) 
0.260 
0.610
 b 
Creatinine (mg/dL) 
40 (100.00) 
0 (0.00) 
80 (100.00) 
0 (0.00) 
- 
- 
eGFR (mL/min/1.73 m^2) 
37 (92.50) 
3 (7.50) 
74 (92.50) 
6 (7.50) 
0.000 
1.000
 c 
WBC (1000/μl) 
40 (100.00) 
0 (0.00) 
80 (100.00) 
0 (0.00) 
- 
- 
RBC (million/μl) 
40 (100.00) 
0 (0.00) 
80 (100.00) 
0 (0.00) 
- 
- 
HGB (g/dL) 
40 (100.00) 
0 (0.00) 
80 (100.00) 
0 (0.00) 
- 
- 
HCT (%) 
40 (100.00) 
0 (0.00) 
80 (100.00) 
0 (0.00) 
- 
- 
MCHC (g/dL) 
40 (100.00) 
0 (0.00) 
79 (98.75) 
1 (1.25) 
0.504 
1.000 
c 
Platelet (1000/uL) 
35 (87.50) 
5 (12.50) 
74 (92.50) 
6 (7.50) 
0.801 
0.503
 c 
RDW-CV (%) 
33 (82.50) 
7 (17.50) 
73 (91.25) 
7 (8.75) 
1.981 
0.226
 c 
a Data are presented as N (%) 
b p-value was determined by using Chi-square (χ2) test (two-tailed) 
c p-value was determined by using Fisher’s exact test (two-tailed) 
 
 
 

 
 
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Table 14. Comparison of different T lymphocyte types between the NS and SC 
groups. 
Parameter 
NS (n=40) a 
SC (n=80) a 
p-value b 
CD3+ T cell (%) 
39.39 (17.38) 
43.09 (20.01) 
0.401 
CD4+ T cell (%) 
42.37 (16.52) 
51.06 (27.31) 
0.044* 
CD8+ T cell (%) 
28.51 (17.31) 
32.47 (17.62) 
0.183 
Regulatory T lymphocyte (%) 
0.081 (0.264) 
0.125 (0.136) 
0.298 
a Data were presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test; *p<0.05 
 
 
 

 
 
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Table 15. Comparison of Pro-inflammatory and anti-inflammatory cytokines between 
NS and SC groups 
Parameter 
NS (n=40) a 
SC (n=80) a 
p-value b 
IL-6 (pg/mL) 
0.47 (1.04) 
7.64 (20.48) 
0.000*** 
IL-10 (pg/mL) 
3.45 (3.73) 
0.50 (3.94) 
0.001** 
IL-17 (pg/mL) 
32.25 (106.22) 
9.38 (51.58) 
0.014* 
TNF-α (pg/mL) 
5.97 (9.81) 
12.63 (56.05) 
0.014* 
TGF-β (ng/mL) 
22.52 (9.10) 
6.36 (15.52) 
0.000*** 
a Data are presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test ; # p<0.1, *p<0.05, 
**p<0.01, ***p<0.001 
 
 
 

 
 
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Table 16. Comparison of antioxidant activity between NS and SC groups 
Parameter 
NS (n=40) a 
SC (n=80) a 
p-value b 
CAT (U/mL) 
38.84 (18.97) 
29.82 (30.55) 
0.028* 
GPx (U) 
111.30 (17.70) 
98.87 (30.85) 
0.004** 
SOD (U/mL) 
191.3 (12.0) 
188.6 (115.1) 
0.514 
a Data are presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test ; # p<0.1, *p<0.05, 
**p<0.01, ***p<0.001 
 
 
 

 
 
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Table 17. Comparison of anabolic hormone between NS and SC groups 
Parameter 
NS (n=40) a 
SC (n=80) a 
p-value b 
DHEA-S (μg/mL) 
107.80 (82.19) 
90.57 (62.45) 
0.082 # 
a Data are presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test ; # p<0.1, *p<0.05, 
**p<0.01, ***p<0.001 
 
 
 

 
 
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Table 18. Comparison of variety metals level in the serum between NS and SC groups 
Metal (μg/L) 
NS (n=36) a 
SC (n=78) a 
p-value b 
Na 
2840438 (496896) 
2730763 (498577) 
0.118 
Mg 
43673(7005) 
43277 (8358) 
0.897 
K 
2724861 (514371) 
2813224(613906) 
0.223 
Mn 
11.45 (4.28) 
11.35 (5.18) 
0.946 
Fe 
406895 (95802) 
397900 (70831) 
0.714 
Ni 
1.42 (0.67) 
1.80 (1.02) 
0.006** 
Cu 
752.0 (119.7) 
761.0 (136.0) 
0.737 
Zn 
6298 (1961) 
6103 (1394) 
0.209 
Sr 
27.80 (9.18) 
21.42 (10.55) 
0.548 
As 
6.35 (8.59) 
5.91 (5.69) 
0.670 
Se 
403.0 (90.7) 
365.4 (96.9) 
0.007** 
Mo 
1.15 (0.84) 
0.97 (0.67) 
0.699 
Cd 
0.56 (0.31) 
0.59 (0.33) 
0.562 
Sb 
4.38 (1.05) 
5.19 (2.63) 
0.019* 
Ba 
0.93 (0.67) 
0.89 (0.53) 
0.644 
Pb 
17.03 (9.93) 
14.14 (9.88) 
0.045* 
a Data were presented as median (IQR) 
b Statistical results were determined by using Mann Whitney test; *p<0.05, **p<0.01 
 
 
 
 

 
 
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Figure 3. Alterations of gut microbiota composition between control group (NS) 
and Sarcopenia Control (SC). 
(A & B) Box-and-whisker plots show α-diversity indices (Shannon index and 
Observed features index) comparing NS and SC groups. (C) β-diversity assessed 
using Bray-Curtis distance and visualized by PCoA. (D) Bar plot showing the relative 
abundance of the top 20 bacterial genera. (E) Heatmap showing taxa with significant 
linear discriminant analysis (LDA) scores (>3.0), indicating genera that are 
differentially abundant between SC and LcS, as identified by LEfSe. The heatmap 
displays Z-score–normalized relative abundance values. For each genus, the relative 
abundance was averaged within each group and then standardized using Z-score 
transformation across groups. Deep red indicates higher relative abundance; deep 
green indicates lower abundance. Only genus-level taxa are shown, and they are 
presented in alphabetical order for clarity and consistency with the main text. (F) 
Heatmap showing the relative abundance of the top 35 gut microbiota genera across 
individual subjects. Each vertical column represents one subject, and the color 
intensity indicates the abundance level. Red denotes higher relative abundance. 

 
 
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Figure 4. MetagenomeSeq analysis of gut microbiota compared NS and SC group. 
Bacterial genus that were significantly more abundant between NS and SC group with high adherence (FDR < 0.05) by metagenomeSeq test, 
including  (a)Oscillibacter, (b)Tyzzerella, (c)Bifidobacterium, (d)Anaerobutyricum, (e)Anaerofilum, (f)Duncaniella, (g)Gluceribacter, 
(h)Lachnospira, and (i)Subdoligranulum. 

 
 
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Table 19. Differentially Abundant Genera Between NS and SC Identified by LEfSe and MetagenomeSeq 
Category 
Increased 
Decreased 
(1) Both LEfSe and MetagenomeSeq  
Bifidobacterium 
(2) LEfSe only 
Shigella, Muribaculum 
Catenibacterium 
(3) MetagenomeSeq only 
Anaerobutyricum, 
Anaerofilum, 
Gluceribacter, 
Lachnospira, Oscillibacter 
Duncaniella, Subdoligranulum, Tyzzerella 
The direction of change is indicated relative to the NS group, with genera classified as significantly increased or decreased in the SC group. 
Results are categorized based on detection by (1) both LEfSe and MetagenomeSeq, (2) LEfSe only, or (3) MetagenomeSeq only. 

 
 
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Influence of Fermented Milk Containing Lactobacillus casei Strain Shirota intervention: 
Table 20 Comparison of the results of anthropometric measurements between pre- and post-intervention in the SC and LcS groups using 
bioelectrical impedance analysis (BIA). 
 
SC (n=40)
 a 
LcS (n=40) a 
SC v.s. LcS (Post-test) 
Parameter 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c  
Calf circumference (cm) 
32.00 (3.15) 
32.00 (2.65) 
0.457 
31.50 (1.90) 
31.50 (2.00) 
0.718 
0.289 
Body height (cm) 
154.3 (16.3) 
154.3 (16.3) 
0.250 
153.0 (9.0) 
153.5 (8.8) 
0.987 
0.473 
Body weight (kg) 
49.00 (8.00) 
49.50 (9.63) 
0.935 
49.00 (8.35) 
49.00 (6.83) 
0.876 
0.360 
BMI (kg/m2) 
20.84 (3.95) 
20.85 (3.29) 
0.836 
20.69 (2.67) 
20.89 (2.72) 
0.798 
0.567 
SMI (kg/m2) 
5.40 (0.55) 
5.55 (0.75) 
0.027* 
5.30 (0.50) 
5.40 (0.70) 
0.169 
0.078 
Skeletal muscle mass (kg) 
17.55 (2.80) 
17.80 (3.38) 
0.275 
17.60 (2.45) 
17.75 (2.57) 
0.362 
0.308 
Soft lean mass (kg) 
31.30 (4.67) 
31.70 (4.45) 
0.997 
31.4 (3.67) 
31.85 (3.83) 
0.113 
0.473 
Fat free mass (kg) 
33.45 (4.85) 
33.75 (5.75) 
0.593 
33.25 (3.98) 
33.90 (4.03) 
0.134 
0.256 
Protein (kg) 
6.45 (0.98) 
6.50 (1.15) 
0.285 
6.50 (0.88) 
6.60 (0.95) 
0.285 
0.429 
Fat (kg) 
15.15 (6.97) 
15.10 (6.95) 
0.480 
15.45 (4.45) 
15.05 (5.42) 
0.167 
0.851 
Intracellular water (kg) 
14.95 (2.12) 
15.15 (2.18) 
0.157 
15.00 (1.95) 
15.15 (2.15) 
0.235 
0.301 
Extracellular water (kg) 
9.55 (1.50) 
9.65 (1.68) 
0.052 
9.45 (1.18) 
9.65 (1.43) 
0.182 
0.389 
Total body water (kg) 
24.45 (3.77) 
24.75 (3.97) 
0.004** 
24.5(2.92) 
24.90 (2.92) 
0.087 
0.248 
Whole body phase angle (
∘) 
4.50 (0.48) 
4.70 (0.58) 
0.047* 
4.50 (0.68) 
4.45 (0.68) 
0.763 
0.060 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test; *p<0.05, **p<0.01 

 
 
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c Statistical results were determined by using Mann Whitney test 
 
 

 
 
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Table 21. Comparison of physical performance between pre- and post-intervention in the SC and LcS groups. 
 
SC (n=40) a 
LcS (n=40) a 
SC v.s. LcS (Post-test) 
Parameter 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c  
Handgrip (kg) 
17.77 (7.31) 
18.95 (6.10) 
0.169 
17.84 (5.80) 
19.07 (6.64) 
0.004** 
0.989 
Chair stand test (sec) 
14.39 (5.31) 
14.95 (6.69) 
0.319 
13.90 (5.31) 
12.31 (6.39) 
0.027* 
0.003** 
Walk speed (m/s) 
0.830 (0.336) 
0.825 (0.245) 
0.191 
0.860(0.296) 
1.077(0.408) 
0.038* 
0.009** 
TUG test (sec) 
12.14 (4.63) 
12.05 (5.01) 
0.727 
11.96 (3.99) 
9.06 (4.96) 
0.001** 
0.170 
SPPB score (pt) 
10 (6.00) 
9 (6.00) 
0.310 
10 (3.00) 
11 (3.00) 
0.006** 
0.015* 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test; *p<0.05, **p<0.01 
c Statistical results were determined by using Mann Whitney test; *p<0.05, **p<0.01 
 
 

 
 
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Table 22. Comparison of pre- and post-intervention physical activity using IPAQ between SC and LcS groups 
Physical 
activity 
SC group (n=40) a 
p-value b 
LcS group (n=40) a 
p-value b 
Post-test (SC v.s. LcS) 
Pre-test 
(n=40) 
Post-test 
(n=40) 
Pre-test 
(n=40) 
Post-test 
(n=40) 
Chi-square 
(χ
2) 
p-value c 
High 
10 (25) 
9 (22.5) 
0.392 
14 (35) 
13 (32.5) 
0.557 
1.360 
0.507 
Medium 
23 (57.5) 
25 (62.5) 
21 (52.5) 
20 (50) 
Low 
7 (17.5) 
6 (15) 
5 (12.5) 
7 (17.5) 
a Data are presented as N (%) 
b p-value was determined by using Mcnemar’s test 
c p-value was determined by using Pearson chi square (X2) test  
 

 
 
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Table 23. Comparison of physical activity pre- and post-intervention using MNA between SC and LcS groups. 
MNA score 
SC group (n=40) a 
p-value b 
LcS group (n=40) a 
p-value b 
Post-test 
(SC v.s. LcS) 
Pre-test 
(n=40) 
Post-test 
(n=40) 
Pre-test 
(n=40) 
Post-test 
(n=40) 
Chi-square (χ
2) 
p-value c 
Well-nourished 34 (85.00) 
37 (92.50) 
0.375 
32 (80.00) 
38 (95.00) 
0.031* 
0.213 
1.000 
At risk of 
malnourished 
6 (15.00) 
3 (7.50) 
8 (20.00) 
2 (5.00) 
Malnourished 
0 (0.00) 
0 (0.00) 
0 (0.00) 
0 (0.00) 
a Data are presented as N (%) 
b p-value was determined by using Mcnemar’s test; *p<0.05 
c p-value was determined by using Fisher’s exact test (two-tailed) 
 
 
Table 24. Comparison of nutritional assessment using 24-hour recall methods pre- and post-intervention, and between SC and LcS groups. 
24 hour-recall methods 
SC group (n=40) a 
p-value c 
LcS group (n=40) ab 
p-value c 
Post-test (SC v.s. LcS) 
Pre-test 
Post-test 
Pre-test 
Post-test 
p-value d 
Total calories (kcal) 
1296 (460) 
1400 (476) 
0.281 
1228 (427) 
1257 (395) 
0.248 
0.273 
Carbohydrate (%) 
168.6 (76.5) 
179.3 (75.5) 
0.261 
139.5 (61.9) 
160.5 (45.2) 
0.056 
0.373 
Protein (%) 
53.35 (21.92) 
55.50 (21.50) 
0.981 
58.15 (31.68) 
57.53 (27.27) 
0.065 
1.000 
Fat (%) 
48.7 (26.28) 
51.25 (36.37) 
0.381 
47 (22.03) 
51.75 (24.73) 
0.381 
0.387 
a Data are presented as median (IQR) 
b Data include the nutrient content from Yakult 300 LIGHT 
c Statistical results were determined by using Wilcoxon matched-pairs signed rank test 
d Statistical results were determined by using Mann Whitney test 

 
 
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Table 25. Comparison of constipation symptom severity using PAC-SYM for pre- and post-intervention, and between SC and LcS groups. 
PAC-SYM 
SC group (n=40) a 
p-value b 
LcS group (n=40) a 
p-value b 
Post-test (SC v.s. LcS) 
Pre-test 
Post-test 
Pre-test 
Post-test 
p-value c 
Abdominal 
0 (1) 
0 (1) 
0.842 
0 (1) 
0 (0) 
0.049* 
0.024* 
Rectal 
0 (1) 
0 (2) 
0.704 
0 (1) 
0 (1) 
0.379 
0.484 
Stools 
0 (0) 
0 (0) 
0.844 
0 (0.75) 
0 (0) 
0.220 
0.883 
Total 
1 (3.75) 
1 (3.75) 
0.709 
1 (3.00) 
0 (1.00) 
0.035* 
0.192 
a Data are presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test; *p<0.05 
c Statistical results were determined by using Mann Whitney test; *p<0.05 
 

 
 
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Table 26. Comparison of biochemical parameters Pre and Post intervention in the SC 
group. 
Biochemical parameter 
SC group (n=40) a 
p-Value b 
 
Pretest (n=40) 
Posttest (n=40) 
 
 
Normal 
Abnormal 
Normal 
Abnormal 
 
TSH 
38 (95) 
2 (5) 
39 (97.5) 
1 (2.5) 
1 
Free T4 
32 (80) 
8 (20) 
31 (77.5) 
9 (22.5) 
1 
hsCRP (mg/dL) 
39 (97.5) 
1 (2.5) 
39 (97.5) 
1 (2.5) 
1 
Albumin (g/dL) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
Pre-albumin (mg/dL) 
40 (100) 
0 (0) 
39 (97.5) 
1 (2.5) 
- 
CHOL (mg/dL) 
25 (62.5) 
15 (37.5) 
27 (67.5) 
13 (32.5) 
0.5 
HDL-C (mg/dL) 
34 (85) 
6 (15) 
33 (82.5) 
7 (17.5) 
1 
LDL-C (mg/dL) 
17 (42.5) 
23 (57.5) 
21 (52.5) 
19 (47.5) 
0.289 
TG (mg/dL) 
37 (92.5) 
3 (7.5) 
38 (95) 
2 (5) 
1 
HbA1c (%) 
18 (45) 
22 (55) 
18 (45) 
22 (55) 
1 
GLU(AC) (mg/dL) 
20 (50) 
20 (50) 
21 (52.5) 
19 (47.5) 
1 
Insulin (uIU/mL) 
40 (100) 
0 (0) 
39 (97.5) 
1 (2.5) 
- 
HOMA-IR 
27 (67.5) 
13 (32.5) 
30 (75) 
10 (25) 
0.375 
AST (U/L) 
39 (97.5) 
1 (2.5) 
40 (100) 
0 (0) 
- 
ALT (U/L) 
39 (97.5) 
1 (2.5) 
40 (100) 
0 (0) 
- 
BUN (mg/dL) 
31 (77.5) 
9 (22.5) 
32 (80) 
8 (20) 
1 
Creatinine (mg/dL) 
38 (95) 
2 (5) 
38 (95) 
2 (5) 
1 
eGFR (mL/min/1.73 m^2) 
37 (92.5) 
3 (7.5) 
37 (92.5) 
3 (7.5) 
1 
WBC (1000/μl) 
37 (92.5) 
3 (7.5) 
37 (92.5) 
3 (7.5) 
1 
RBC (million/μl) 
26 (65) 
14 (35) 
26 (65) 
14 (35) 
1 
HGB (g/dL) 
36 (90) 
4 (10) 
37 (92.5) 
3 (7.5) 
1 
HCT (%) 
29 (72.5) 
11 (27.5) 
25 (62.5) 
15 (37.5) 
0.289 
MCHC (g/dL) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
Platelet (1000/uL) 
37 (92.5) 
3 (7.5) 
37 (92.5) 
3 (7.5) 
1 
RDW-CV (%) 
38 (95) 
2 (5) 
39 (97.5) 
1 (2.5) 
1 
a Data are presented as N (%) 
b p-value was determined by using McNemar's test 
 

 
 
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Table 27. Comparison of biochemical parameters Pre and Post intervention in LcS group 
Biochemical parameter 
LcS group a 
p-Value b 
 
Pretest (n=40) 
Posttest (n=40) 
 
 
Normal 
Abnormal 
Normal 
Abnormal 
 
TSH 
39 (97.5) 
1 (2.5) 
38 (95) 
2 (5) 
1 
Free T4 
40 (100) 
0 (0) 
39 (97.5) 
1 (2.5) 
- 
hsCRP (mg/dL) 
40 (100) 
0 (0) 
39 (97.5) 
1 (2.5) 
- 
Albumin (g/dL) 
38 (95) 
2 (5) 
40 (100) 
0 (0) 
- 
Pre-albumin (mg/dL) 
38 (95) 
2 (5) 
35 (87.5) 
5 (12.5) 
0.375 
CHOL (mg/dL) 
22 (55) 
18 (45) 
19 (47.5) 
21 (52.5) 
0.549 
HDL-C (mg/dL) 
36 (90) 
4 (10) 
36 (90) 
4 (10) 
1 
LDL-C (mg/dL) 
18 (45) 
22 (55) 
12 (30) 
28 (70) 
0.18 
TG (mg/dL) 
33 (82.5) 
7 (17.5) 
33 (82.5) 
7 (17.5) 
1 
HbA1c (%) 
16 (40) 
24 (60) 
15 (37.5) 
25 (62.5) 
1 
GLU(AC) (mg/dL) 
27 (67.5) 
13 (32.5) 
30 (75) 
10 (25) 
0.453 
Insulin (uIU/mL) 
39 (97.5) 
1 (2.5) 
39 (97.5) 
1 (2.5) 
1 
HOMA-IR 
30 (75) 
10 (25) 
33 (82.5) 
7 (17.5) 
0.508 
AST (U/L) 
38 (95) 
2 (5) 
36 (90) 
4 (10) 
0.5 
ALT (U/L) 
39 (97.5) 
1 (2.5) 
38 (95) 
2 (5) 
1 
BUN (mg/dL) 
34 (85) 
6 (15) 
35 (87.5) 
5 (12.5) 
1 
Creatinine (mg/dL) 
37 (92.5) 
3 (7.5) 
37 (92.5) 
3 (7.5) 
1 
eGFR (mL/min/1.73 m^2) 
37 (92.5) 
3 (7.5) 
37 (92.5) 
3 (7.5) 
1 
WBC (1000/μl) 
38 (95) 
2 (5) 
37 (92.5) 
3 (7.5) 
1 
RBC (million/μl) 
26 (65) 
14 (35) 
28 (70) 
12 (30) 
0.727 
HGB (g/dL) 
37 (92.5) 
3 (7.5) 
38 (95) 
2 (5) 
1 
HCT (%) 
32 (80) 
8 (20) 
32 (80) 
8 (20) 
1 
MCHC (g/dL) 
39 (97.5) 
1 (2.5) 
40 (100) 
0 (0) 
- 
Platelet (1000/uL) 
37 (92.5) 
3 (7.5) 
37 (92.5) 
3 (7.5) 
1 
RDW-CV (%) 
35 (87.5) 
5 (12.5) 
36 (90) 
4 (10) 
1 
a Data are presented as N (%) 
b p-value was determined by using McNemar's test 
 
 

 
 
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Table 28. Comparison of biochemical parameters between post-tests of the SC and LcS groups 
Biochemical parameter 
Posttest a 
Chi-square 
(χ2) 
p-Value 
 
SC group (n=40) 
LcS group (n=40) 
 
 
Normal 
Abnormal 
Normal 
Abnormal 
  
  
TSH 
39 (97.50) 
1 (2.50) 
38 (95.50) 
2 (5.00) 
0.346 
1.00c 
Free T4 
31 (77.50) 
9 (22.50) 
39 (97.50) 
1 (2.50) 
7.314 
0.014*c 
hsCRP (mg/dL) 
40 (100) 
0 (0) 
39 (97.50) 
1 (2.50) 
1.013 
1.00c 
Albumin (g/dL) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
- 
Pre-albumin (mg/dL) 
39 (97.50) 
1 (2.50) 
35 (87.50) 
5 (12.50) 
2.883 
0.201c 
CHOL (mg/dL) 
27 (67.50) 13 (32.50) 19 (47.50) 21 (52.50) 
3.274 
0.07b 
HDL-C (mg/dL) 
31 (77.50) 
9 (22.50) 
36 (90.00) 
4 (10.00) 
2.296 
0.225c 
LDL-C (mg/dL) 
21 (52.50) 19 (47.50) 12 (30.00) 28 (70.00) 
4.178 
0.041*b 
TG (mg/dL) 
38 (95.00) 
2 (5.00) 
33 (82.50) 
7 (17.50) 
3.13 
0.154c 
HbA1c (%) 
21 (52.50) 19 (47.50) 19 (47.50) 21 (52.50) 
0.464 
0.496b 
GLU(AC) (mg/dL) 
21 (52.50) 19 (47.50) 30 (75.00) 10 (25.00) 
4.381 
0.036*b 
Insulin (uIU/mL) 
39 (97.50) 
1 (2.50) 
39 (97.50) 
1 (2.50) 
0.000 
1.000c 
HOMA-IR 
21 (52.50) 19 (47.50) 21 (52.50) 19 (47.50) 
0.000 
1.000b 
AST (U/L) 
40 (100) 
0 (0) 
36 (90.00) 
4 (10.00) 
4.211 
0.116c 
ALT (U/L) 
40 (100) 
0 (0) 
38 (95.00) 
2 (5.00) 
2.051 
0.494c 
BUN (mg/dL) 
32 (80.00) 
8 (20.00) 
35 (87.50) 
5 (12.50) 
0.827 
0.363b 
Creatinine (mg/dL) 
37 (92.50) 
3 (7.50) 
40 (100) 
0 (0) 
- 
- 
eGFR (mL/min/1.73 m^2) 
37 (92.50) 
3 (7.50) 
37 (92.50) 
3 (7.50) 
0.000 
1.000c 
WBC (1000/μl) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
- 
RBC (million/μl) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
- 
HGB (g/dL) 
39 (97.50) 
1 (2.50) 
40 (100) 
0 (0) 
- 
- 
HCT (%) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
- 
MCHC (g/dL) 
40 (100) 
0 (0) 
40 (100) 
0 (0) 
- 
- 
Platelet (1000/uL) 
37 (92.50) 
3 (7.50) 
37 (92.50) 
3 (7.50) 
0.000 
1.000c 
RDW-CV (%) 
39 (97.50) 
1 (2.50) 
36 (90.00) 
4 (10.00) 
1.92 
0.359c 
a Data are presented as N (%) 
b p-value was determined by using Chi-square (χ2)test (two-tailed) ; *p<0.05 
c p-value was determined by using Fisher’s exact test (two-tailed); *p<0.05 

 
 
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Table 29. Comparison of T lymphocyte subsets pre- and post-intervention, and between SC and LcS groups using flow cytometry. 
 
SC (n=40) a 
LcS (n=40) a 
SC v.s. LcS (Post-test) 
Parameter 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c 
CD3+ T cell (%) 
41.50 (20.93) 43.06 (20.79) 
0.755 
46.52 (20.72) 45.15 (24.83) 
0.289 
0.148 
CD4+ T cell (%) 
46.65 (22.12) 45.95 (26.79) 
0.715 
53.68 (27.27) 42.78 (29.88) 
0.050 
0.989 
CD8+ T cell (%) 
31.83 (20.94) 32.20 (20.42) 
0.715 
32.47 (14.80) 32.97 (18.30) 
0.283 
0.235 
Regulatory T cell (%) 
0.124 (0.121) 0.098 (0.217) 
0.512 
0.127 (0.187) 0.113 (0.156) 
0.881 
0.315 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test 
c Statistical results were determined by using Mann Whitney test 
 
 
 

 
 
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Table 30. Comparison of pro- and anti-inflammatory cytokines pre- and post-intervention, and between SC and LcS groups using ELISA.  
 
SC (n=40) a 
LcS (n=40) a 
SC v.s. LcS (Post-test) 
Parameter 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c 
IL-6 (pg/mL) 
11.34 (21.58) 
7.06 (18.14) 
0.678 
5.12 (19.36) 
7.70 (13.50) 
0.916 
0.923 
IL-10 (pg/mL) 
0.50 (2.95) 
0.50 (2.83) 
0.644 
0.50 (8.32) 
0.50 (4.66) 
0.890 
0.364 
IL-17 (pg/mL) 
9.38 (11.81) 
9.38 (8.27) 
0.946 
9.38 (104.62) 
9.38 (22.53) 
0.491 
0.068 
TGF-β (ng/mL) 
4.85 (13.77) 
5.05 (12.12) 
0.581 
16.14 (17.96) 
16.60 (17.10) 
0.162 
0.128 
TNF-α (pg/mL) 
20.09 (81.64) 
29.30 (76.18) 
0.072 
7.71 (36.61) 
7.94 (37.98) 
0.034* 
0.019* 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test; *p<0.05 
c Statistical results were determined by using Mann Whitney test; *p<0.05 
 

 
 
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Table 31. Comparison of antioxidant enzyme activity pre- and post-intervention, and between SC and LcS groups using the ELISA method. 
 
SC (n=40) a 
LcS (n=40) a 
SC v.s. LcS (Post-test) 
Parameter 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c 
CAT (U/mL) 
33.81 (27.34) 
35.38 (29.18) 
0.942 
27.55 (28.50) 
26.16 (26.37) 
0.454 
0.028* 
GPx (U) 
103.30 (30.29) 105.80 (16.88) 
0.805 
89.86 (28.48) 
105.20 (23.49) 
0.016* 
0.755 
SOD (U/mL) 
193.1 (140.2) 
199.6 (100.6) 
0.396 
166.4 (107.6) 
159.3 (118.9) 
0.237 
0.447 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test; *p<0.05 
c Statistical results were determined by using Mann Whitney test; *p<0.05 
 
 
 

 
 
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Table 32. Comparison of anabolic hormone levels pre- and post-intervention, and between SC and LcS groups using the ELISA method. 
 
SC (n=40) a 
LcS (n=40) a 
SC v.s. LcS (Post-test) 
Parameter 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c 
DHEA-S (μg/mL) 
96.00 (61.80) 
80.76 (70.70) 
0.265 
81.24 (66.43) 
81.99 (70.19) 
0.411 
0.799 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test 
c Statistical results were determined by using Mann Whitney test 
 
 
 

 
 
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Table 33. Comparison of metal concentrations pre- and post-intervention, and between SC and LcS groups using ICP-MS. 
 
SC (n=40) a 
LcS (n=38) a 
SC v.s. LcS (Post-test) 
Metal 
(μg/L) 
Pre-test 
Post-test 
p-value b 
Pre-test 
Post-test 
p-value b 
p-value c 
Na 
2664544 (1059721) 
2739663 (622123) 
0.685 
2795566 (439564) 
2322972 (664245) 
0.000*** 
0.000*** 
Mg 
42241 (9293) 
42351 (13154) 
0.627 
43956 (7848) 
36202 (13662) 
0.000*** 
0.010* 
K 
2807697 (474408) 
2852263 (752869) 
0.476 
2821855 (687371) 
2262375 (799729) 
0.000*** 
0.002** 
Mn 
10.80 (5.35) 
11.83 (6.90) 
0.349 
11.56 (4.78) 
9.47 (5.30) 
0.002** 
0.033* 
Fe 
398423 (82033) 
387504 (79431) 
0.314 
397900 (70183) 
327065 (137461) 
0.000*** 
0.010* 
Ni 
1.92 (0.88) 
1.62 (1.28) 
0.020* 
1.57 (0.97) 
1.10 (0.92) 
0.003** 
0.026* 
Cu 
719.2 (121.1) 
697.4 (162.8) 
0.066 
762.7 (117.6) 
644.9 (167.6) 
0.000*** 
0.039* 
Zn 
6298 (1427) 
6105 (2070) 
0.536 
5869 (1199) 
4691 (1903) 
0.000*** 
0.011* 
Sr 
21.62 (9.93) 
20.38 (11.50) 
0.206 
19.75 (11.00) 
14.00 (6.17) 
0.000*** 
0.000*** 
As 
4.57 (5.67) 
5.12 (4.76) 
0.860 
6.36 (6.79) 
6.19 (6.13) 
0.440 
0.690 
Se 
363.7 (82.8) 
381.6 (45.7) 
0.189 
368.9 (110.2) 
355.3 (120.5) 
0.447 
0.183 
Mo 
0.98 (0.65) 
1.14 (1.01) 
0.131 
0.98 (0.70) 
1.01 (0.72) 
0.691 
0.128 
Cd 
0.62 (0.35) 
0.66 (0.47) 
0.261 
0.56 (0.29) 
0.52 (0.22) 
0.272 
0.015* 
Sb 
5.12 (2.53) 
5.09 (2.36) 
0.625 
5.23 (3.37) 
4.52 (2.12) 
0.048* 
0.020* 
Ba 
0.89 (0.58) 
1.14 (1.44) 
0.030* 
0.90 (0.51) 
0.64 (0.57) 
0.027* 
0.000*** 
Pb 
14.14 (11.32) 
15.43 (14.50) 
0.722 
13.61 (9.80) 
10.03 (6.62) 
0.000*** 
0.004** 
a Data were presented as median (IQR) 
b Statistical results were determined by using Wilcoxon matched-pairs signed rank test; *p<0.05, **p<0.01, ***p<0.001 
c Statistical results were determined by using Mann Whitney test; *p<0.05, **p<0.01, ***p<0.001

 
 
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Gut microbiota composition and microbiota-derived Metabolite 
Analysis: 
 

 
 
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Figure 5. Alterations of gut microbiota composition between Sarcopenia Control (SC) and Sarcopenia + Lacticaseibacillus casei Shirota (LcS) groups. 
 
(A & B) Box-and-whisker plots show α-diversity indices (Shannon index and Observed features index) comparing SC and LcS groups. 
(C) β-diversity assessed using Bray-Curtis distance and visualized by PCoA. 
(D) Bar plot showing the relative abundance of the top 20 bacterial genera. 
(E) Heatmap showing the relative abundance of the top 35 gut microbiota genera across individual subjects. Each vertical column represents one subject, and 
the color intensity indicates the abundance level. Red denotes higher relative abundance. 
(F) Heatmap showing taxa with significant linear discriminant analysis (LDA) scores (>3.0), indicating genera that are differentially abundant between SC and 
LcS, as identified by LEfSe. The heatmap displays Z-score–normalized relative abundance values. For each genus, the relative abundance was averaged within 
each group and then standardized using Z-score transformation across groups. Deep red indicates higher relative abundance; deep green indicates lower 
abundance. Only genus-level taxa are shown, and they are presented in alphabetical order for clarity and consistency with the main text. 
 
 

 
 
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Figure 6. MetagenomeSeq analysis of gut microbiota compared SC group and LcS group. 
Bacterial genus that were significantly more abundant between SC group and LcS group with high adherence (FDR < 0.05) by metagenomeSeq test, including  
(a)Lacticaseibacillus, 
(b)Muribaculum, 
(c)Neglectibacter, 
(d)Bifidobacterium, 
(e)Caproiciproducens, 
(f)Lentihominibacter, 
(g)Desulfovibrio, 

 
 
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(h)Faecalibacillus, (i)Mogibacterium, (j)Adlercreutzia, (k)Cloacibacillus, (l)[Clostridium], (m)Colidextribacter, (n)Ellagibacter, (o)Enterobacter, (p)Gehongia, 
(q)Guopingia, (r)Lacrimispora, (s)Merdimonas, (t)Negativibacillus, (u)Oscillibacter, and (v)Sinanaerobacter. 
 
 
 

 
 
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Table 34. Differentially Abundant Genera Between LcS and SC Identified by LEfSe and MetagenomeSeq 
 
Category 
Increased 
Decreased 
(1) Both LEfSe and MetagenomeSeq 
Bifidobacterium, Lacrimispora, Lacticaseibacillus, 
Cloacibacillus, 
Gehongia, 
Muribaculum, 
Negativibacillus, Oscillibacter 
 
(2) LEfSe only 
Megamonas, Streptococcus, 
Klebsiella, 
Mediterraneibacter, 
Megasphaera, 
Odoribacter 
 
(3) MetagenomeSeq only 
[Clostridium], 
Enterobacter, 
Faecalibacillus, 
Merdimonas, Neglectibacter 
Adlercreutzia, Caproiciproducens, Colidextribacter, 
Desulfovibrio, 
Ellagibacter, 
Guopingia, 
Lentihominibacter, Mogibacterium, Sinanaerobacter 
The direction of change is indicated relative to the SC group, with genera classified as significantly increased or decreased in the LcS group. Results are 
categorized based on detection by (1) both LEfSe and MetagenomeSeq, (2) LEfSe only, or (3) MetagenomeSeq only. 
 
 
 
 

 
 
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Table 35 . Comparison of metabolite analysis among NS, SC, and LcS groups using LC-MS/MS. 
 
Group 
NS 
SC 
LcS 
Kruskal-Wallis 
test 
NS vs SC 
NS vs LcS 
SC vs LcS 
 
median (IQR) 
p-value a 
Acetic acid (nM) 
2,038.82 (1,561.23) 
1,357.79 (1,147.08) 
1,635.15 (824.05) 
0.0025** 
** 
ns 
ns 
Propionic acid (nM) 
1,789.35 (1,467.82) 
1,582.41 (1,418.59) 
1,711.42 (1,091.85) 
0.289 
ns 
ns 
ns 
Butyric acid (nM) 
698.14 (769.52) 
468.83 (509.72) 
450.01 (637.81) 
0.0433 
ns 
ns 
ns 
Isobutyric acid (nM) 
101.89 (72.15) 
100.83 (56.38) 
83.58 (63.58) 
0.3817 
ns 
ns 
ns 
Valeric acid (nM) 
161.66 (141.77) 
161.87 (169.79) 
128.13 (135.24) 
0.22 
ns 
ns 
ns 
Isovaleric acid (nM) 
61.46 (58.32) 
199.85 (139.80) 
88.11 (90.96) 
<0.0001*** 
*** 
ns 
*** 
TMAO (nM) 
1.02 (0.23) 
1.07 (1.20) 
1.06 (0.49) 
0.824 
ns 
ns 
ns 
IPA (nM) 
361.75 (292.59) 
372.62 (264.91) 
479.96 (463.63) 
0.378 
ns 
ns 
ns 
a Statistical results were determined by using Kruskal-Wallis test and Dunn's Multiple Comparison Test; ns: non-significant, *p<0.05, **p<0.01, 
***p<0.001 

 
 
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Figure 7. The spearman correlations between gut microbiota and physical 
examinations 
The heat map shows the Spearman correlations between gut microbiota and physical 
examinations in SC (n=40) and LcS (n=40) subjects; *p<0.05  
 
 
Handgrip
Chair stand test
Walk speed
SPPB score
TUG test
Odoribacter
Muribaculum
Parabacteroides
Klebsiella
Bifidobacterium
Veillonella
Megasphaera
Lacticaseibacillus
Negativibacillus
Oscillibacter
Gehongia
Lacrimispora
*
*
*
*
-0.2
0
0.2

 
 
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Figure 8. the Spearman correlations between gut microbiota and body 
compositions 
The heat map displayed the Spearman correlations between gut microbiota and body 
compositions in SC (n=40), and LcS (n=40) subjects; *p<0.05.  
 
 
 
 
 
 
 
 
 
 
Body high
Body weight
BMI
SMI
Skeletal muscle mass
Soft lean mass
Fat Free Mass
Protein
Fat
Total Body Water
Intracellular Water
Extracellular Water
Whole Body Phase Angle
Odoribacter
Muribaculum
Parabacteroides
Klebsiella
Bifidobacterium
Veillonella
Megasphaera
Lacticaseibacillus
Negativibacillus
Oscillibacter
Gehongia
Lacrimispora
*
*
*
*
*
*
*
*
*
* *
* * *
*
* *
*
* * *
* *
* * * *
* *
-0.2
0
0.2

 
 
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Figure 9. The Spearman correlations between gut microbiota and cytokines, 
DHEA-S and CD4 lymphocyte 
The heat map displayed the Spearman correlations between gut microbiota and 
cytokines, DHEA-S and CD4 lymphocyte in SC (n=40), and LcS (n=40) subjects; 
*p<0.05. 
 
 
IL-6
IL-10
IL-17
TGF-β
TNF-α
SOD
CAT
GPx
DHEA-S
CD4
Odoribacter
Muribaculum
Parabacteroides
Klebsiella
Bifidobacterium
Veillonella
Megasphaera
Lacticaseibacillus
Negativibacillus
Oscillibacter
Gehongia
Lacrimispora
*
*
*
-0.2
0
0.2

 
 
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Figure 10. The Spearman correlations between gut microbiota and biochemical 
parameters  
The heat map displayed the Spearman correlations between gut microbiota and 
biochemical parameters in SC (n=40), and LcS (n=40) subjects; *p<0.05. 
 
TSH
Free T4
hsCRP
Albumin
Pre-albumin
CHOL
HDL-C
LDL-C
TRIG
HbA1c
GLU
insulin
HOMA-IR
GOT
GPT
BUN
CREA
eGFR
WBC
RBC
HGB
HCT
MCHC
Platelet
RDW-CV
Odoribacter
Muribaculum
Parabacteroides
Klebsiella
Bifidobacterium
Veillonella
Megasphaera
Lacticaseibacillus
Negativibacillus
Oscillibacter
Gehongia
Lacrimispora
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
-0.2
0
0.2

 
 
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Figure 11. The Spearman correlations between gut microbiota and heavy metals 
The heat map displayed the Spearman correlations between gut microbiota and heavy 
metals in SC (n=40), and LcS (n=40) subjects; *p<0.05.  
 
 
Na
Mg
K
Mn
Fe
Ni
Cu
Zn
Sr
As
Se
Mo
Cd
Sb
Ba
Pb
Odoribacter
Muribaculum
Parabacteroides
Klebsiella
Bifidobacterium
Veillonella
Megasphaera
Lacticaseibacillus
Negativibacillus
Oscillibacter
Gehongia
Lacrimispora
*
*
*
*
*
*
* * *
*
*
*
* *
*
*
*
* *
*
*
*
-0.4
-0.2
0
0.2

 
 
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Figure 12. The Spearman correlations between gut microbiota and metabolites 
The heat map displayed the Spearman correlations between gut microbiota and 
metabolites in SC (n=40), and LcS (n=40) subjects; *p<0.05.
 
 

 
 
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Appendix 
 
Appendix I 
 
 
 

 
 
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Appendix II 
 

 
 
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Appendix Ⅲ 
 

 
 
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Appendix IV 
 
 

 
 
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Appendix Ⅴ 
 
 
 
 

 
 
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Appendix Ⅵ 
 
 

 
 
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Appendix Ⅶ 
 

 
 
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Appendix VIII 
 

 
 
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Appendix Ⅸ