A fructose-based meal challenge to assess metabotypes and their metabolic risk profile: A randomized, crossover, controlled trial

Metabotyping: a tool for identifying subgroups for tailored nutrition advice

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

Urolithins: a Comprehensive Update on their Metabolism, Bioactivity, and Associated Gut Microbiota

Urolithins, metabolites produced by the gut microbiota from the polyphenols ellagitannins and ellagic acid, are discovered by the research group in humans almost 20 years ago. Pioneering research suggests urolithins as pleiotropic bioactive contributors to explain the health benefits after consuming ellagitannin-rich sources (pomegranates, walnuts, strawberries, etc.). Here, this study comprehensively updates the knowledge on urolithins, emphasizing the review of the literature published during the last 5 years. To date, 13 urolithins and their corresponding conjugated metabolites (glucuronides, sulfates, etc.) have been described and, depending on the urolithin, detected in different human fluids and tissues (urine, blood, feces, breastmilk, prostate, colon, and breast tissues). There has been a substantial advance in the research on microorganisms involved in urolithin production, along with the compositional and functional characterization of the gut microbiota associated with urolithins metabolism that gives rise to the so-called urolithin metabotypes (UM-A, UM-B, and UM-0), relevant in human health. The design of in vitro studies using physiologically relevant assay conditions (molecular forms and concentrations) is still a pending subject, making some reported urolithin activities questionable. In contrast, remarkable progress has been made in the research on the safety, bioactivity, and associated mechanisms of urolithin A, including the first human interventions.

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

BACKGROUND AND AIMS

Evidence suggests that people react differently to the same diet due to inter-individual differences. However, few studies have investigated variation in response to dietary interventions based on individuals' baseline metabolic characteristics. This study aims to examine the differential reaction of metabotype subgroups to an OGTT and a dietary fiber intervention.

METHODS AND RESULTS

We assigned 356 healthy participants of an OGTT sub-study and a 12-week dietary fiber intervention sub-study within the enable cluster to three metabotype subgroups previously identified in the KORA F4 study population. To explore the association between plasma glucose level and metabotype subgroups, we used linear mixed models adjusted for age, sex, and physical activity. Individuals in different metabotype subgroups showed differential responses to OGTT. Compared to the healthy metabotype (metabotype 1), participants in intermediate metabotype (metabotype 2) and unfavorable metabotype (metabotype 3) had significantly higher plasma glucose concentrations at 120 min after glucose bolus (β = 7.881, p = 0.005; β = 32.79, p < 0.001, respectively). Additionally, the linear regression model showed that the Area under the curve (AUC) of plasma glucose concentrations was significantly different across the metabotype subgroups. The associations between metabotype subgroups and metabolic parameters among fiber intervention participants remained insignificant in the multivariate-adjusted linear model. However, the metabotype 3 had the highest mean reduction in insulin, cholesterol parameters (TC, LDLc, and non-HDLc), and systolic and diastolic blood pressure at the end of the intervention period.

CONCLUSION

This study supports the use of the metabotype concept to identify metabolically similar subgroups and to develop targeted dietary interventions at the metabotype subgroup level for the primary prevention of diet-related diseases.

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

Despite the high human interindividual variability in response to (poly)phenol consumption, the cause-and-effect relationship between some dietary (poly)phenols (flavanols and olive oil phenolics) and health effects (endothelial function and prevention of LDL oxidation, respectively) has been well established. Most of the variables affecting this interindividual variability have been identified (food matrix, gut microbiota, single-nucleotide-polymorphisms, etc.). However, the final drivers for the health effects of (poly)phenol consumption have not been fully identified. At least partially, these drivers could be (i) the (poly)phenols ingested that exert their effect in the gastrointestinal tract, (ii) the bioavailable metabolites that exert their effects systemically and/or (iii) the gut microbial ecology associated with (poly)phenol metabolism (i.e., gut microbiota-associated metabotypes). However, statistical associations between health effects and the occurrence of circulating and/or excreted metabolites, as well as cross-sectional studies that correlate gut microbial ecologies and health, do not prove a causal role unequivocally. We provide a critical overview and perspective on the possible main drivers of the effects of (poly)phenols on human health and suggest possible actions to identify the putative actors responsible for the effects.