An individual's immune and metabolic status is coupled to consumed carbohydrates. Complex carbohydrates that are not digested by human enzymes may influence host biology by impacting microbiota composition and function, or act in a yet-unknown microbiota-independent manner. Prebiotics offer a promising safe route to influence host health, possibly via the microbiota. However, it remains largely unknown to what extent immune function and metabolism can be modulated by prebiotics.
The objective of this study is to define the impact of a prebiotic supplement on microbiome, immune system, and metabolic status in older adults. This study will determine the degree to which a prebiotic supplement can 1) regulate immune status and function including reducing chronic, systemic inflammation as assessed by high dimensional immune profiling, 2) alter microbiota composition and function, 3) impact the microbiota metabolites-potential normalizers of metabolic and immune dysfunction, and 4) alter metabolic markers.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
QUADRUPLE
Enrollment
98
Placebo product
Prebiotic supplement
Stanford University
Stanford, California, United States
Immune status and function
Change from baseline in Cytokine Response Score (CRS) at 6 weeks. The CRS is a single composite measure of cell-type specific activation of signaling pathways from ex vivo cytokine stimulation of blood samples. This provides a measure of immune response capacity which may be an indicator of immune fitness. The CRS will be calculated as described in Shen-Orr et al, Cell Systems, 2016. The CRS is the sum of 15 age-associated normalized cytokine responses identified in Shen-Orr et. al: In CD8+ T cells: IFNα pSTAT1, pSTAT3, pSTAT5; IL-6 pSTAT1, pSTAT3, pSTAT5; IFNγ pSTAT1; IL-21 pSTAT1; In CD4+ T cells: IFNα pSTAT5; IL-6 pSTAT5; In B cells: IFNα pSTAT1; in monocytes: IL-10 pSTAT3; IFNγ pSTAT3; IFNα pSTAT3; IL-6 pSTAT3. Each feature is calculated as the fold change of the protein in the stimulated condition relative to its level in the unstimulated condition. That value is then normalized to the feature's range: normalized = (x - xmin)/xmax. The 15 normalized values are summed for the CRS.
Time frame: Baseline and 6 weeks
Microbiota composition
Change from baseline in alpha diversity at 6 weeks. We will be using number of observed sequence variants ("species") determined by standard 16S rRNA amplicon sequencing (V3-V5 region followed by DADA2 to define error-corrected sequence variants) as our primary metric of alpha diversity. Higher alpha diversity is better. The units are the # of sequence variants.
Time frame: Baseline and 6 weeks
Microbiota function
Change from baseline in composite of short-chain fatty acids (SCFA) concentration (ug/g stool: acetate + propionate + butyrate) at 6 weeks.
Time frame: Baseline and 6 weeks
Weight
Change from Baseline in weight at 6 weeks.
Time frame: Baseline and 6 weeks
Waist Circumference
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Change from Baseline in waist circumference at 6 weeks.
Time frame: Baseline and 6 weeks
Blood pressure
Change from Baseline in blood pressure at 6 weeks.
Time frame: Baseline and 6 weeks
Total Cholesterol
Change from Baseline in total cholesterol at 6 weeks.
Time frame: Baseline and 6 weeks
Triglycerides
Change from Baseline in triglycerides at 6 weeks.
Time frame: Baseline and 6 weeks
HDL-cholesterol
Change from Baseline in HDL-cholesterol at 6 weeks.
Time frame: Baseline and 6 weeks
Fasting Glucose
Change from Baseline in fasting glucose at 6 weeks.
Time frame: Baseline and 6 weeks
Fasting Insulin
Change from Baseline in fasting insulin at 6 weeks.
Time frame: Baseline and 6 weeks