Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study

Abstract

There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39–0.90 for AHEI and OR = 0.60, 95% CI = 0.40–0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.

Evaluation of the metabotype concept after intervention with oral glucose tolerance test and dietary fiber-enriched food: An enable study

“…However, when the population was stratified into five clusters, one of the clusters characterized by lower serum 25(OH)D showed a significant decrease in insulin, homeostatic model assessment scores, and C-reactive protein. Likewise, in our previous analyses of the KORA FF4 cohort, we found that a significant association between dietary pattern and T2D detected in all participants remained significant only in the unfavorable subgroups when the analysis was stratified by metabotype [34]. Together, these studies, along with our results, demonstrate that the metabotyping concept can be used to identify metabolically similar subpopulations, which may benefit from a targeted dietary intervention.…”

Main drivers of (poly)phenol effects on human health: metabolite production and/or gut microbiota-associated metabotypes?

“…Molecules such as cholesterol, glucose, and xenobiotics (dietary, pharmacological and environmental compounds), among many others, can show distinctive metabotypes. 43,59,[100][101][102][103] Therefore, the unequivocal identification of metabotypes associated with the metabolism of (poly)phenols by the gut microbiota requires limiting the definition to this specific context, i.e., a metabolic phenotype that gives rise to characteristic metabolites derived from the catabolism of specific (poly)phenols by a particular gut microbial ecology in terms of composition and functionality. 59 Overall, a metabotype associated with the gut microbiota is defined by a qualitative criterion (i.e., producer vs non-producer of specific metabolites) and not by a quantitative criterion (i.e., high producer vs low producer), since the production gradient could be affected by external factors (diet, motility of GI tract, food matrix, sample collection time, etc.).…”

Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation

“…Therefore, all nutrition-related analyses were limited to this sample size. The Alternate Healthy Eating Index 2010 (AHEI, modified to exclude trans fats) and Mediterranean Diet Score 2003 (MDS) were calculated for this subsample of 1442 individuals as performed by Wawro et al [39][40][41].…”