Thomas M.S. Wolever

To define the type of dietary fibre of fibre analogue with the greatest potential use in diabetic treatment, groups of four to six volunteers underwent 50-g glucose tolerance tests (GTT) with and without the addition of either guar, pectin, gum tragacanth, methylcellulose, wheat bran, or cholestyramine equivalent to 12 g fibre. The addition of each substance significantly reduced blood glucose concentration at one or more points during the GTT and generally reduced serum insulin concentrations. The greatest flattening of the glucose response was seen with guar, but this effect was abolished when hydrolysed non-viscous guar was used. The reduction in the mean peak rise in blood glucose concentration for each substance correlated positively with its viscosity (r = 0.926; P less than 0.01), as did delay in mouth-to-caecum transit time (r = 0.885; P less than 0.02). Viscous types of dietary fibre are therefore most likely to be therapeutically useful in modifying postprandial hyperglycaemia.

The glycaemic index (GI) concept was originally introduced to classify different sources of carbohydrate (CHO)-rich foods, usually having an energy content of >80 % from CHO, to their effect on post-meal glycaemia. It was assumed to apply to foods that primarily deliver available CHO, causing hyperglycaemia. Low-GI foods were classified as being digested and absorbed slowly and high-GI foods as being rapidly digested and absorbed, resulting in different glycaemic responses. Low-GI foods were found to induce benefits on certain risk factors for CVD and diabetes. Accordingly it has been proposed that GI classification of foods and drinks could be useful to help consumers make 'healthy food choices' within specific food groups. Classification of foods according to their impact on blood glucose responses requires a standardised way of measuring such responses. The present review discusses the most relevant methodological considerations and highlights specific recommendations regarding number of subjects, sex, subject status, inclusion and exclusion criteria, pre-test conditions, CHO test dose, blood sampling procedures, sampling times, test randomisation and calculation of glycaemic response area under the curve. All together, these technical recommendations will help to implement or reinforce measurement of GI in laboratories and help to ensure quality of results. Since there is current international interest in alternative ways of expressing glycaemic responses to foods, some of these methods are discussed.

BACKGROUND/OBJECTIVES: High dietary fibre intakes may protect against obesity by influencing colonic fermentation and the colonic microbiota. Though, recent studies suggest that increased colonic fermentation contributes to adiposity. Diet influences the composition of the gut microbiota. Previous research has not evaluated dietary intakes, body mass index (BMI), faecal microbiota and short chain fatty acid (SCFA) in the same cohort. Our objectives were to compare dietary intakes, faecal SCFA concentrations and gut microbial profiles in healthy lean (LN, BMI⩽25) and overweight or obese (OWOB, BMI>25) participants. DESIGN: We collected demographic information, 3-day diet records, physical activity questionnaires and breath and faecal samples from 94 participants of whom 52 were LN and 42 OWOB. RESULTS: Dietary intakes and physical activity levels did not differ significantly between groups. OWOB participants had higher faecal acetate (P=0.05), propionate (P=0.03), butyrate (P=0.05), valerate (P=0.03) and total short chain fatty acid (SCFA; P=0.02) concentrations than LN. No significant differences in Firmicutes to Bacteroides/Prevotella (F:B) ratio was observed between groups. However, in the entire cohort, Bacteroides/Prevotella counts were negatively correlated with faecal total SCFA (r=-0.32, P=0.002) and F:B ratio was positively correlated with faecal total SCFA (r=0.42, P<0.0001). Principal component analysis identified distinct gut microbiota and SCFA-F:B ratio components, which together accounted for 59% of the variation. F:B ratio loaded with the SCFA and not with the microbiota suggesting that SCFA and F:B ratio vary together and may be interrelated. CONCLUSIONS: The results support the hypothesis that colonic fermentation patterns may be altered, leading to different faecal SCFA concentrations in OWOB compared with LN humans. More in-depth studies looking at the metabolic fate of SCFA produced in LN and OWOB participants are needed in order to determine the role of SCFA in obesity.