Health Status
Conditions
Keywords
Fibre, Gut microbiome, Meal challenge, Microvasculature
Brief summary
Improving healthy physiological processes through nutritional intervention, as opposed to restoring physiology after disease occurrence, is an important new avenue for the reduction of disease burden in the population. A relatively new target for interventions is the gut microbiome. Dietary fibre is a nutritional intervention shown to alter gut microbiome and function. The present study aims to elucidate the relationship between microbiome modulation with dietary fibre and health. In order to assess health improvement, a meal challenge will be given to characterize the physiological processes and their resilience to challenge in healthy volunteers before and after microbiome modulation.
Detailed description
The gut microbiome has been extensively implicated as an organ involved in various physiological processes such as nutrient and drug metabolism, microbial protection and immunomodulation. The gut microbiome educates the host immune system, promotes homeostasis and protects against systemic inflammation, among other things through production of short-chain fatty acids (SCFAs). An altered microbiome is also involved in inducing low-grade systemic inflammation by translocation of bacterial lipopolysaccharide (LPS) through the intestinal lining. Additionally, the gut microbiome produces trimethylamine (TMA), which when oxidized to trimethylamine N-oxide (TMAO) is documented as an indicator of endothelial dysfunction and cardiovascular health risk. The goal of the present study is to further clarify the relationship between the gut microbiome, homeostasis, immunity and health. This will be achieved by introducing an intervention known to alter gut microbiome characteristics, dietary fibre, and measuring its effect on the gut microbiome on the one hand and the response to metabolic challenge on the other. Fibre mixtures consisting of indigestible carbohydrates have been shown to alter the composition and function of the gut microbiome. Fibre functions as a substrate for fermentation, creating SCFAs, and as a food source for bacterial commensals regarded as beneficial. Thus, fibre can shift the balance of microbial species in the gut towards beneficial commensals and away from potential pathogens. Moreover, these changes in composition and function of the microbiome can feasibly affect integrity of intestinal lining, TMA production and various other processes, therefore exerting an effect on low-grade inflammation, cardiovascular and metabolic health. The gut microbiome can be analysed using 16s RNA sequencing, quantifying the relative abundance of various bacterial species, and by measuring SCFAs in human plasma, quantifying their production by bacteria in the gut. In this study, we will integrate a third method to measure the gut microbiome. The I-screen, developed by TNO, is a platform in which the in vivo microbiome composition can be mimicked in an in vitro system, allowing for experimental analysis of the effects of compounds and ingredients on the microbiome. With this method, the processes and conditions affecting microbiome composition can be assessed more closely, possibly clarifying specific relationships between intervention and microbiome composition. The present study aims to assess the effects of this microbiome modulation by evaluating the response to a metabolic challenge, quantified through measurement of a metabolic and inflammatory biomarker panel to create a composite outcome called 'resilience'. This phenotypical flexibility test (PhenFlex, PFT), consisting of a mixed meal with protein, fat and glucose, induces a systemic response which when analysed allows for sensitive assessment of subtle health benefits in otherwise healthy subjects. Results of the PFT are presented as a composite of multiple biomarkers grouped by physiological processes such as inflammation and liver metabolism, creating the 'axes' of a 'health space'. Earlier research shows that selected dietary products affected inflammatory processes, oxidative stress and metabolism, based on dynamic responses after fat load. Other research has shown that the challenge test concept is able to reveal previously unidentified correlations between specific nutrients and health-related processes, and that decreased phenotypic flexibility as measured by PFT can be used to identify people that might benefit from health interventions. Finally, in a human volunteer study with whole grain wheat products researchers were able to show a positive effect on diverse composite markers of resilience, including low grade inflammation, after 12 weeks of exchange of refined wheat for whole grain wheat consumption. By measuring the effect of our intervention on the gut microbiome with several tools, as well as using the challenge concept for quantifying health, this study is well positioned to provide insight in the specific mechanisms of interaction between microbiome and host, as well as create new evidence-based avenues for the improvement of health.
Interventions
OTHERplacebo
Powder consisting of 13 g of digestible carbohydrates with similar appearance as the investigational fibre mixture.
DIETARY_SUPPLEMENTFibre mixture
The study product is a fibre mixture consisting of a mix of 10 g of Acacia Gum and 3 g of carrot fibre taken p.o. o.d. in powder form for a total of approximately 10 g of dietary fibre per day.
Sponsors
Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO)
Centre for Human Drug Research, Netherlands
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
TRIPLE (Subject, Caregiver, Investigator)
Masking description
Double-blind
Intervention model description
Placebo or intervention, wash-out, intervention or placebo
Eligibility
Sex/Gender
ALL
Age
45 Years to 70 Years
Healthy volunteers
Yes
Inclusion criteria
1. Signed informed consent prior to any study-mandated procedure. 2. Healthy male or female subjects, between 45 and 70 years of age, inclusive. 3. Female subjects must be of non-childbearing potential (postmenopausal for at least 12 months prior to screening or documented surgically sterile). 4. BMI 25-30 kg/m2, inclusive 5. Fibre intake below recommended limits as assessed by dietary fibre intake short food frequency questionnaire (DFI-FFQ) (16). 6. Has the ability to communicate well with the Investigator in the Dutch language and willing to comply with the study restrictions.
Exclusion criteria
1. Evidence of any active or chronic disease or condition that could interfere with, or for which the treatment of might interfere with, the conduct of the study, or that would pose an unacceptable risk to the subject in the opinion of the investigator (following a detailed medical history, physical examination, vital signs (systolic and diastolic blood pressure, pulse rate, body temperature) and 12-lead electrocardiogram (ECG)). Minor deviations from the normal range may be accepted, if judged by the Investigator to have no clinical relevance. 2. Chronic diseases that can affect study parameters, including but not limited to metabolic syndrome, chronic obstructive pulmonary disease, diabetes mellitus, auto-immune disease, cardiovascular disease, cerebrovascular disease, gastrointestinal disease or history of abdominal surgery with removal of (part of) small or large intestine, or any known condition that can interfere with treatment compliance such as psychiatric disease or drug dependence. 3. Positive Hepatitis B surface antigen (HBsAg), Hepatitis C antibody (HCV Ab), or human immunodeficiency virus antibody (HIV Ab) at screening. 4. Systolic blood pressure (SBP) greater than 180 or less than 90 mm Hg, and diastolic blood pressure (DBP) greater than 120 or less than 50 mm Hg at screening. 5. Abnormal findings in the resting ECG at screening defined as: 1. QTcF\> 450 for males or QTcF\>470 for females or QTcF \< 300 ms; 2. Personal or family history of congenital long QT syndrome or sudden death; 3. Evidence of atrial fibrillation, atrial flutter, complete branch block, Wolf-Parkinson-White Syndrome, or history of cardiac pacemaker. 6. Use of antibiotics, antacids, laxatives, statins, anti-diarrheal, immunomodulatory or antidiabetic medication \<3 months before start of study. 7. Use of any medication or vitamin, mineral, herbal, and dietary supplements within 7 days of study product administration, or less than 5 half-lives (whichever is longer). Exceptions will only be made if the rationale is clearly documented by the investigator. 8. Vegan, macrobiotic, slimming or medically prescribed diet up to 3 months prior to the first administration. 9. History of food allergies or intolerances or any confirmed significant allergic reactions (urticarial or anaphylaxis) against any drug or multiple documented drug allergies. 10. Participation in an investigational drug or device study within 3 months prior to first dosing. 11. History of abuse of addictive substances (alcohol, illegal substances) or current use of more than 21 units alcohol per week, drug abuse, or regular user of sedatives, hypnotics, tranquillisers, or any other addictive agent, or positive test for drugs of abuse at screening or pre-dose. 12. Active smoker up to 15 years prior to the screening visit. 13. Loss or donation of blood over 500 mL within three months (males) or four months (females) prior to screening or intention to donate blood or blood products during the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Total cholesterol | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to total cholesterol. |
| Microbiome changes | Change from Baseline microbiome changes at week 4 | Microbiome changes measured using 16S rRNA sequencing |
| Non-esterified fatty acids (NEFAs) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to non-esterified fatty acids (NEFAs) |
| Glucose | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to glucose |
| Insulin | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to insulin |
| Triglycerides (TG) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to triglycerides (TG) |
| High-density lipoprotein (HDL) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to high-density lipoprotein (HDL) |
| Low-density lipoprotein (LDL) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to low-density lipoprotein (LDL) |
| Interleukin-6 | Change from Baseline at week 32 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to interleukin-6 |
| Interleukin-8 | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to interleukin-8 |
| Interleukin-10 | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to interleukin-10 |
| Tumour necrosis factor alpha (TNF-α) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to tumour necrosis factor alpha (TNF-α). |
| High-sensitivity C-reactive protein (hs-CRP) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to high-sensitivity C-reactive protein (hs-CRP) |
| Serum amyloid A (SAA) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited serum amyloid A (SAA) |
| Alanine aminotransferase (ALT) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to alanine aminotransferase (ALT) |
| Aspartate aminotransferase (AST) | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to aspartate aminotransferase (AST) |
| Gamma-glutamyltransferase (GGT). | Change from Baseline at week 12 | Response to challenge of metabolic and inflammatory biomarkers including but not limited to gamma-glutamyltransferase (GGT). |
Countries
Netherlands
Outcome results
None listed