PerStarch

Research Project, 2024 – 2026

Background

Recent studies have shown that individuals respond metabolically differently to the same diet in acute and extended meal settings (1, 2). Differential response in glycemia, lipid profiles, inflammation biomarkers and appetite have been shown in response to standardized meals using muffins, Phenflex drinks and regular meals (1-5). Individuals with different response patterns may be at different risk of developing chronic diseases such as type 2 diabetes, cardiovascular disease and obesity, as well as their preconditions. Several factors have been dissected to cause the differential responses including genetic set up, gut microbiota, health conditions, anthropometrics, background diet and food properties (6-8). Intensive research is ongoing to identify novel biomarkers that could reflect if a person belongs to a certain response type or risk group (9-11). Such information may have important implications for more precise and more effective early prevention of disease using tailored dietary strategies, i.e. personalized nutrition (11). The amounts and quality of carbohydrates in the diet have major implications for cardiometabolic health and disease. Carbohydrate quality can be indicated by whole grain/refined grain, dietary fibre content, glycemic index (GI) and amount of sugar in the diet (12). It is well established that the GI will have impact on acute postprandial glycemic responses in both diabetic and healthy individuals and that a lowering of postprandial glucose responses to meals are associated with lowered risk of diseases among diabetic individuals, but the link to long-term health effects is more controversial among healthy or individuals with pre-conditions of diabetes (13). Recent studies have suggested that long-term consumption of personalized diet that is optimized at an individual level to keep the blood sugar levels as low as possible during the day and to avoid fluctuations is more beneficial compared to a healthy Mediterranean diet when it comes to risk factors of T2D among pre-diabetic individuals(14). Moreover, in our own studies we have investigated the long-term metabolic effects of a low vs a high GI diet in the context of a healthy Mediterranean diet pattern context and found that low GI diet is supported (13), but that there are large inter-individual differences in glycemic responses to standardized meals with regular foods. We identified 2 response clusters from standardized meal studies that were differentially associated with T2D risk factors and we also found anther two differential response clusters based on continuous glucose data that are differentially associated with cardiometabolic risk profiles (5). In the first case, we found that the metabolically beneficial response cluster was associated with higher abundance of butyrate producing bacterial geneses at base-line, but determinants of response groups remains to be revealed (5). Recently, several studies have shown that the relative presence of specific bacterial geneses, namely Prevotella and Bacteroides appears associated with differential glycemic (15), lipemic (16) and weight loss response (17) to high cereal fibre interventions . Moreover, the copy number of the enzyme AMY1 that is coding for salivary amylases that degrade starch appears to be associated with metabolically beneficial effects after consumption of cereal fibrer and resistant starch (18). Moreover, studies from Denmark has suggested a beneficial interaction between presence of Prevotella and low copy number of AMY1 for effects on glycemia (18, 19).

Aims and hypotheses

We hypothesized that individuals with high abundance of Prevotella in their fecal microbiota at baseline would have better capacity to ferment slow-digested and resistant starch that may reach colon than individuals with lower abundance of Prevotella. We further hypothesize that the effect of will be even stronger among individuals with a low copy number of AMY1 enzymes. This will be due to larger amounts of starch will reach colon where it will be fermented and that this will beneficially affect postprandial glycemia. Based on above we hypothesize that postprandial metabolic responses (with emphasis on glycemic responses) will differ due to base-line abundance of Prevotella and copy number of AMY1 and that response clusters can be identified and that type of starch can be matched to response type based on a meal test and thus provide guidance towards healthier personalized eating. The overall aim is therefore to characterize differential metabolic responses to resistant vs slowly vs fast digested starch meals across individuals with different microbiota and copy-number of AMY1. Specifically, we will: A) Investigate differential metabolic response (glycemia, lipemia, and inflammation biomarkers) in response to meal tolerance tests based on fast, slow or resistant starch meal tests served for breakfast, lunch and dinner across individuals with high abundance of Prevotella or high abundance of Bacteroides with low/high AMY1 copy number. B) Evaluate metabolic effects of chronic consumption of slow- and resistant starch daily for 2 weeks vs fast digested starch across different enterotypes and in individuals with low/high AMY1 copy numbers.