If the data is normally distributed, differences between variables (postprandial glucose, insulin, C-peptide, triglyceride and 24 h glucose) of intervention groups will be analysed using multiple mixed-effects linear regression method allowing for repeated measurements from the same individuals. Treatment condition and the participant will be regarded as a fixed factor and a random factor respectively. Prior to analysis all outcomes will be tested for assumption of normality. In case of non-normality, data transformation will be applied and if this is not satisfactory the analysis will be carried out using non-parametric equivalent techniques. Models will be corrected for age, gender, physical activity and baseline measurements, diabetes history, drug intake and diet.