The gut microbiome plays an important role in regulation of metabolic processes, including digestion, absorption, and synthesis of bioactive molecules that signal physiological host mechanisms. Changes in the human gut microbiome are associated with type 2 diabetes and insulin resistance. Water-soluble dietary fibres like inulin and beta-glucan are fermented in the colon, and beta-glucan increases viscosity. Blueberries improve insulin sensitivity through an antioxidant effect. A cobiotic, consisting of purified inulin, sugar-free blueberry pomace extract, and an oat preparation of purified beta-glucan was developed for twice a day (bid) consumption as a smoothie drink to repair the gastrointestinal dysbiosis in type 2 diabetes. A 30-year-old man presented with new onset type 2 diabetes and a fasting glucose (FBS) of 375 mg/dl. Metformin 500 mg bid was initiated and increased to 1 g bid after 1 week. During the first 9 days of metformin treatment, he developed diarrhoea, but his FBS only dropped to 325 mg/dl. The cobiotic bid was added on the 9th day of metformin treatment, and after 2 days, his FBS dropped to 175 mg/dl. After 8 weeks on metformin and the cobiotic, his blood sugar was 100 mg/dl and he lost 5.5 kg. His stools became soft and formed on the cobiotic, reverted to diarrhoea when off of it for 2 days, and returned to normal on resuming the cobiotic formulation. Metformin is a safe, effective and inexpensive generic medication favouring weight loss, recommended as initial treatment of type 2 diabetes by the American Diabetes Association. However, a 20% incidence of diarrhoea limits its tolerability. A safe food supplement that can increase the efficacy of metformin and its tolerability, as occurred in this case report, would have significant positive public health consequences. A controlled clinical trial of the cobiotic with metformin is planned.
Objective: Obese patients respond differently to weight loss interventions. No efficient diagnostic tool exists to separate obese patients into subtypes as a means to improve prediction of response to interventions. We aimed to separate obese subjects into distinct subgroups using microarray technology to identify gene expression-based subgroups to predict weight loss. Design: A total of 72 obese men and women without family history of diabetes were enrolled in the study; 52 were treated with ephedra and caffeine (E þ C) and 20 with placebo for 8 weeks. Adipose and skeletal muscle tissue biopsies were performed at baseline. RNA sample pairs were labeled and hybridized to oligonucleotide microarrays. Quantile normalization and gene shaving were performed, and a clustering algorithm was then applied to cluster subjects based on their gene expression profile. Clusters were visualized using heat maps and related to weight changes. Results: Cluster analysis of gene expression data revealed two distinct subgroups of obesity and predicted weight loss in response to the treatment with E þ C. One cluster ('red') decreased to 96.87±2.35% body weight, and the second cluster ('green') decreased to 95.59 ± 2.75% body weight (Po0.05). 'Red' cluster had less visceral adipose tissue mass (2.77 ± 1.08 vs 3.43±1.49 kg; Po0.05) and decreased size of the very large fat cells (1.45±0.61 vs 2.16±1.74 ml; Po0.05) compared to 'green' cluster. Gene expression for both skeletal muscle and adipose tissue was also different between clusters. Conclusions: Our study provides the first evidence that the combined approach of gene expression profiling and cluster analysis can identify discrete subtypes of obesity, these subtypes have different physiological characteristics and respond differently to an adrenergic weight loss therapy. This brings us that into an era of personalized treatment in the obesity clinic.
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