Effect of Fermented Milk Containing Lactobacillus Casei Strain Shirota in Sarcopenia Elderly

Study Overview
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Brief Summary
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Sarcopenia, which refers to the progressive loss of skeletal muscle mass and strength, shares many characteristics with other disease states typically associated with risks of falling and fracture, including osteoporosis, frailty, and obesity. Sarcopenia often is linked to an increase in connective tissue, muscle steatosis, impaired muscle metabolism, an increase in inflammatory markers (e.g., TNF-a and IL-6), an increased stiffness of myofibers, slower kinetics in establishing myosin-actin crossing bridges, increased oxidative stress, mitochondria dysfunction, hormonal imbalance, and decreased capillary flow. In addition to the Lactobacillus casei strain, Shirota is a well-known probiotic strain that has been approved and is generally recognized as safe by the United States Food and Drug Administration. L. casei strain Shirota (LCS) has been suggested to confer health benefits. Investigators found that LCS decelerated age-related muscle loss via ensuring mitochondrial function in Senescence-Accelerated Mouse Prone 8 (SAMP8) mice. Investigators also found that 3-Indolepropionic Acid (IPA) is a microbiota-derived metabolite from a healthy intestinal gut. IPA is also a protective factor for the progression of chronic kidney disease in the Chinese population. This clinical trial focuses on the effect of fermented milk containing LCS on sarcopenia in elderly Taiwanese individuals. Investigators focus on the topic of the interaction of LCS with the diversity of the gut microbiota, microbiota-derived metabolites, and protein utilization. The proposal will have four research goals. First, investigators try to investigate whether long term supplementation LCS could restructure the gut microbiota composition and gut microbial metabolites to against Aging related -Gut Dysbiosis in elderly. Second, investigators also try to link the LCS play an important role on muscle loss and alternation of gut microbiota composition. Third, investigators try to study the LCS effect of muscle deterioration with aging, and highlight the two underpinning mechanisms (ROS and protein utilization) regulating declines in muscle mass and function. Fourth, Since IPA is important Microbiota-derived metabolites in health gut, investigators try to investigate whether LCS could play an important role on modulation of IPA production in GI. Fifth, investigators hope that investigators can through gut microbiota composition found some selective microbiota clusters perform positively correlation with IPA.

Detailed Description
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1. Subject Enrollment The definition of sarcopenia is based on the algorithm of Asian Working Group for Sarcopenia (AWGS). Muscle mass: measured by using Inbody S10 and standardized by height, shown by skeletal muscle index [SMI=appendicular muscle mass (kg)/height (m2)]. Low muscle mass was defined as: Men: SMI <7.0 kg/m2 Women: SMI <5.4 kg/m2 Handgrip strength: measured by electronic hand grip dynamometer. Low handgrip strength was defined as: Men: <28 kg Women: <18 kg Limb strength: measured by Time for 5 times for chair stand method. Low limb strength was defined as: Time for 5 times for chair stand: ≧12s Sarcopenia was defined by (1) and one of (2), or (3) 2. Study intervention Test beverages included Fermentation Milk fermented with L. casei strain Shirota YIT9029 (LCS) provided by the Yakult Company (Taipei, Taiwan). The beverages were distributed and stored at temperatures ranging from 0-10°C. The Fermentation Milk contained LCS at more than 3x10^8 CFU per 100 ml bottle. The participants would take 2 bottles per day (104 Kcal/day). Study intervention should be taken twice daily at approximately the same time. All milks would be sent to participant weekly (14 bottles/week). 3. Study assessment 3-1 Anthropometric measurement and Body Composition Assessment Anthropometric measurement data was detected height, weight, waist circumference, arm muscle circumference, hip circumference, and calf circumference using measure tape and weight scale. Body composition was detected using bioelectrical impedance analysis (BIA) as a non-invasive test instrument. 3-2 BIA Subjects stand on met without shoes and socks. Arms do not touch the trunk part of body, and are spread naturally to a 15 degree angle away from trunk. The hand electrodes are marked THUMB for the thumb and MIDDLE for the middle finger. The foot electrodes should be positioned between examinee's anklebone and heel. Electric current was supplied from the electrodes on the tips of the heels of both feet and the fingertips of both hands. 3-3 Muscle strength Grip strength measured in standard conditions with a well-studied model of a handheld dynamometer with reference populations can be a reliable surrogate for more complicated measures of muscle strength in the lower arms or legs. The measurement uses electronic hand grip dynamometer. Low handgrip strength is defined in Men: <28 kg ; Women: <18kg. Limb strength measured by Time for 5 times for chair stand. Subjects sat and stood for five times, and calculated time by timer. Low limb strength is defined in time for Time for 5 times for chair stand: ≧12s. 3-4 Physical performance Physical performance was assessed by using 6-meter usual gait speed, TUG, and SPPB. 3-5 Gait speed Participants were instructed to walk from a standing start at a pace that was normal and comfortable for them or to walk as fast as participants could until participants reached the end of the marked path. A trained examiner walked behind the participant and stopped timing when the participant's foot contacted the floor at the end of the walking course. Participants were provided rest breaks as needed throughout the testing session. Low Physical performance is defined < 1 m/s. 3-6 TUG Participants were instructed to stand up from the chair, walk 3 meters forward and go back to sit on the chair. Low Physical performance is defined ≧ 20s. 3-7 SPPB Participants were followed to SPPB to do the test. Low Physical performance is defined ≦ 9 scores. 3-8 Total bacteria count, Gut Microbiota Composition and Microbiota-derived Metabolite Analysis 3-8-1 16S V1-V2 Sequencing Total genome DNA from samples was extracted from stool samples using the CTAB/SDS method. The V1-V2 regions of 16S rRNA gene were selected for pyrosequencing analysis. 3-8-2 16S rRNA Gene Sequence Analysis 16S rRNA gene sequence data were processed with QIIME v 1.8.0 using default parameters. The sequences were clustered into operational taxonomic units (OTUs) at 97% similarity and then assigned Greengenes taxonomy using the uclust consensus taxonomy classifier. 3-8-3 Fecal Short Chain Fatty Acids The fecal samples were diluted in deionized water and homogenized. For short chain fatty acid (SCFA) analysis, a supernatant was used. Diethyl ether extraction was carried out using the method of Adorno et al. 3-8-4 IPA and TMAO Determination Investigators analyzed serum and fecal levels of IPA using LC-MS/MS (4000 QTRAP, City, State, USA). The plasma TMAO concentrations were detected by stable isotope (DLM4779-1, Andover, MA, USA) dilution liquid chromatography tandem mass spectrometry (Ke et al., 2018). 3-8-5 Total bacterial count by qPCR Total bacterial counts were determined by qPCR analysis according to the method described by Shima et al. (Beneficial Microbes, 2019) using a forward primer (UniF: GTGSTGCAYGGYYGTCGTCA) and a reverse primer (UniR: ACGTCRTCCMCNCCTTCCTC) sets. Faecalibacterium prausnitzii ATCC27768T was used as a standard strain. 3-9 Blood Biochemical Analyses 3-9-1 Blood Sampling Blood samples was collected from the subjects at before-test and after-test. The blood samples were placed in collection tubes containing anticoagulant EDTA and heparin. 3-9-2 Metabolic Parameters All of the metabolic measurements were performed one day prior to the start of the intervention and at the end of intervention. CBC, TSH, Free T4, Vit-D, Total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, fasting glucose, fasting insulin, blood urea, nitrogen, plasma prealbumin, albumin, ALT, AST, HbA1c, HS-CRP, osteocalcin, adiponectin, leptin, serotonin, and creatinine were measured after fasting at least 8 hrs. 3-9-3 Plasma Amino Acid Analysis Investigators used a Hitachi L-8900 Amino Acid Analyzer (Location) to detect and quantify the alanine, phenylalanine, cysteine, aspartic acid, asparagine, glutamic acid, glutamine, glycine, histidine, leucine, isoleucine, lysine, proline, arginine, serine, threonine, valine, tryptophan, tyrosine, and methionine in the plasma samples. 3-9-4 Inflammatory Cytokines Analysis The inflammation-associated serum cytokines TNF-α, TGF-β, IL-6, IL-17, and IL-10 were analyzed using colorimetric kits (Elabscience, China). The procedures followed the kit instructions and were measured using an ELISA reader (Bio Tek, PowerWave XS2, City, State, USA). 3-9-5 Anti-oxidant marker analysis Many anti-oxidant enzymes in human bodies, like Superoxide dismutase (SOD) (Elabscience, China), Glutathione peroxidase (GPx) (biovision, City, State, USA), and Catalase (CAT) (biovision, City, State, USA). Enzymes in the serum were evaluated for the activity by ELISA kit. The procedures followed the kit instructions and were measured by the ELISA reader (Bio Tek, PowerWave XS2, City, State, USA). 3-9-6 Trace elements Approximately 1 mL of each whole blood sample was microwave digested with 3 mL of 65 % nitric acid (Ultrapure Reagent, J.T. Baker). Subsequently, investigators washed the residuals in microwave tubes with 2 % nitric acid and then filtered the digested fluids with 0.22 μm filter. The total filtered solutions were stored in 15 mL centrifuge tubes. The levels of Pb, Cd, As, Hg, Mn, Al, Tl, V, Na, K, Mg, Ca, Fe, Zn, Se were determined using inductively coupled plasma mass spectrometry (ICP-MS; Agilent 7800). 3-9-7 Immune function analysis In T-cells infiltrating skeletal muscle, Regulatory T cells (Tregs) are a major subset. Tregs can regulate inflammation in muscle and promote the regeneration of muscle; however, the catabolic effect of inflammation was promoted by the dysfunctional Tregs in aged muscle.[EbioMedicine. 2019 Nov;49:381-388.] The blood cells were stained with monoclonal antibodies (anti-CD4, -CD25 and -FoxP3) and analyzed by flow cytometry.

Official Title
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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

Conditions
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Sarcopenia, Inflammation, Aging

Intervention / Treatment
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* Dietary Supplement: Yakult light 300


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age between 65 and 85 years No use of hormonal replacement therapy (women) No hospital admissions within the last 3 months Sarcopenia criteria: (1) Muscle mass: measured by using InbodyS10 and standardized by height, shown by skeletal muscle index [SMI=appendicular muscle mass (kg)/height (m2)]. Low muscle mass was defined as: Men: SMI <7.0 kg/m2 Women: SMI <5.4 kg/m2 (2) Handgrip strength: measured by electronic hand grip dynamometer. Low handgrip strength was defined as: Men: <28 kg Women: <18 kg (3) Limb strength: measured by Time for 5 times for chair stand method. Low limb strength was defined as: Time for 5 times for chair stand: ≧12s Sarcopenia was defined by (1), (2) or (3) Exclusion Criteria: Active cancer: currently receiving cancer treatment or have received cancer treatment within the last 3 months Weight change ≥ 5% or weight change ≥ 5 kg within the past 3 months BMI > 35 kg/m2 Disease requiring chronic use of prescription corticosteroids History of ischemic or hemorrhage stroke Unstable or uncontrollable hypertension (>180/110 mmHg) Doing hemodialysis or peritoneal dialysis within the last 3 months Participation in a structured physical exercise training program within the past 2 year; previous use of creatinine supplementation; use of drugs that can affect bone metabolism (e.g., glucocorticoids, bisphosphonates, vitamin D or calcium). Antibiotics were used in the past 3 months. Products of probiotic were used in the past 2 weeks. Living abroad for one month in the past 3 months Hyperthyroidism without medication therapy Allergic to milk

Ages Eligible for Study
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Minimum Age: 65 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Sarcopenia group<br>Yakult light 300 supplementation with sarcopenia patient | Dietary Supplement: Yakult light 300<br>* 2 bottles of fermented Milk containing Lactobacillus casei Strain Shirota per day, Intervention for 12 weeks.<br>|
| No Intervention: Non sarcopenia group<br>no intervention to sarcopenia patient | |
| No Intervention: Control group<br>no intervention to non-sarcopenia people | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Gut microbiota | To analyze changes from baseline gut microbiota by the 16S rRNA Gene Sequence Analysis after 12 weeks intervention. | 12 weeks | 
| Correlation among serum albumin level and serum level of micronutrients | To understand the correlation among serum albumin level and serum level of micronutrients. | 12 weeks | 
| Correlation among 24hr dietary recall and serum level of micronutrients | Calculate total calories, carbohydrate, protein, and fat intake from 24hr dietary recall test, and estimate the correlation between serum level of micronutrients | 12 weeks | 
| Correlation among Food frequence questionnaire and serum level of micronutrients | To understand the correlation among Food frequence questionnaire and serum level of micronutrients. | 12 weeks | 
| Impact of LCS on the change of muscle mass | To analyze the change of muscle mass in kilogram by the bioelectrical impedance analysis (Inbody S10) from baseline after 12 Weeks. | 12 weeks | 
| Impact of LCS on the change of skeletal muscle mass index | To analyze the change of skeletal muscle mass index (skeletal muscle mass/ the square root of height) by the bioelectrical impedance analysis (Inbody S10) from baseline after 12 Weeks. | 12 weeks | 
| Impact of LCS on the change of appendicular skeletal muscle mass index | To analyze the change of appendicular skeletal muscle mass index (appendicular skeletal muscle mass/ the square root of height) by the bioelectrical impedance analysis (Inbody S10) from baseline after 12 Weeks. | 12 weeks | 
| Impact of LCS on nutritional status by phase angle | Study the impact of LCS on nutritional status by phase angle degree in the elderly with sarcopenia. | 12 weeks | 
| Impact of LCS on nutritional status by total cell count | Study the impact of LCS on nutritional status by total cell count in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by body weight | Study the impact of LCS on nutritional status by body weight in kilogram in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by muscle weight | Study the impact of LCS on nutritional status by muscle weight in kilogram in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by mid-arm circumference | Study the impact of LCS on nutritional status by mid-arm circumference in centimeter in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by albumin | Study the impact of LCS on nutritional status by albumin (g/dL) in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by pre-albumin | Study the impact of LCS on nutritional status by pre-albumin (mg/dL) in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by 24hr-dietary recall | Study the impact of LCS on nutritional status by 24hr-dietary recall method in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on nutritional status by MNA questionnaire | Study the impact of LCS on nutritional status by MNA questionnaire score in the elderly with sarcopenia after 12 weeks intervention. | 12 weeks | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Correlations among LCS, Gut Microbiota, IPA, plasma amino acid analysis, fecal SCFAs and fecal ammonia | To understand the correlations among LCS, Gut Microbiota, indole-3-propionic acid (IPA), plasma amino acid analysis, fecal short chain fatty acids (SCFAs) and fecal ammonia. | 12 weeks | 
| Correlations between plasma Pb concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Pb concentration. | 12 weeks | 
| Correlations between plasma Cd concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Cd concentration. | 12 weeks | 
| Correlations between plasma As concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma As concentration. | 12 weeks | 
| Correlations between plasma Hg concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Hg concentration. | 12 weeks | 
| Correlations between plasma Mn concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Mn concentration. | 12 weeks | 
| Correlations between plasma Al concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Al concentration. | 12 weeks | 
| Correlations between plasma Tl concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Tl concentration. | 12 weeks | 
| Correlations between plasma V concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma V concentration. | 12 weeks | 
| Correlations between plasma Na concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Na concentration. | 12 weeks | 
| Correlations between plasma K concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma K concentration. | 12 weeks | 
| Correlations between plasma Mg concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Mg concentration. | 12 weeks | 
| Correlations between plasma Ca concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Ca concentration. | 12 weeks | 
| Correlations between plasma Fe concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Fe concentration. | 12 weeks | 
| Correlations between plasma Zn concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Zn concentration. | 12 weeks | 
| Correlations between plasma Se concentration and Taiwan elderly with sarcopenia | To understand the correlation between incidence of sarcopenia in Taiwan and plasma Se concentration. | 12 weeks | 
| Impact of LCS on the change of reactive oxygen species (ROS) | To analyze the change of the concentration of reactive oxygen species (ROS) by DCFDA / H2DCFDA - Cellular ROS Assay Kit after 12 weeks intervention. | 12 weeks | 
| Impact of LCS on the change of TNF-α concentration | To analyze the change of the concentration of inflammatory biomarkers TNF-α by human TNF-α ELISA kit from baseline to 12 weeks intervention after. | 12 weeks | 
| Impact of LCS on the change of TGF-β concentration | To analyze the change of the concentration of inflammatory biomarkers TGF-β by human TGF-β ELISA kit from baseline to 12 weeks intervention after. | 12 weeks | 
| Impact of LCS on the change of IL-6 concentration | To analyze the change of the concentration of inflammatory biomarkers IL-6 by human IL-6 ELISA kit from baseline to 12 weeks intervention after. | 12 weeks | 
| Impact of LCS on the change of IL-10 concentration | To analyze the change of the concentration of inflammatory biomarkers IL-10 by human IL-10 ELISA kit from baseline to 12 weeks intervention after. | 12 weeks | 
| Impact of LCS on the change of IL-17 concentration | To analyze the change of the concentration of inflammatory biomarkers IL-17 by human IL-17 ELISA kit from baseline to 12 weeks intervention after. | 12 weeks |