Tart Cherry Concentrate Does Not Alter the Gut Microbiome, Glycaemic Control or Systemic Inflammation in a Middle-Aged Population.
Rebecca LearMary F O'LearyLee O'Brien AndersenCorey Carrington HoltChristen Rune StensvoldMark van der GiezenJoanna L BowtellPublished in: Nutrients (2019)
Limited evidence suggests that the consumption of polyphenols may improve glycaemic control and insulin sensitivity. The gut microbiome produces phenolic metabolites and increases their bioavailability. A handful of studies have suggested that polyphenol consumption alters gut microbiome composition. There are no data available investigating such effects in polyphenol-rich Montmorency cherry (MC) supplementation. A total of 28 participants (aged 40-60 years) were randomized to receive daily MC or glucose and energy-matched placebo supplementation for 4 wk. Faecal and blood samples were obtained at baseline and at 4 wk. There was no clear effect of supplementation on glucose handling (Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and Gutt indices), although the Matsuda index decreased significantly in the MC group post-supplementation, reflecting an increase in serum insulin concentration. Contrastingly, placebo, but not MC supplementation induced a 6% increase in the Oral Glucose Insulin Sensitivity (OGIS) estimate of glucose clearance. Serum IL-6 and C reactive protein were unaltered by either supplement. The faecal bacterial microbiome was sequenced; species richness and diversity were unchanged by MC or placebo and no significant correlation existed between changes in Bacteroides and Faecalibacterium abundance and any index of insulin sensitivity. Therefore, 4 weeks of MC supplementation did not alter the gut microbiome, glycaemic control or systemic concentrations of IL-6 and CRP in a middle-aged population.
Keyphrases
- type diabetes
- middle aged
- double blind
- insulin resistance
- blood glucose
- phase iii
- placebo controlled
- metabolic syndrome
- blood pressure
- clinical trial
- physical activity
- open label
- oxidative stress
- machine learning
- ms ms
- skeletal muscle
- artificial intelligence
- big data
- deep learning
- polycystic ovary syndrome
- clinical evaluation