Two Randomized Cross-Over Trials Assessing the Impact of Dietary Gluten or Wholegrain on the Gut Microbiome and Host Metabolic Health

Ibrügger, Sabine; Gøbel, Rikke Juul; Vestergaard, Henrik; Licht, Tine Rask; Frøkiær, Hanne; Linneberg, Allan; Hansen, Torben; Gupta, Ramneek; Pedersen, Oluf; Kristensen, Mette; Lauritzen, Lotte

doi:10.4172/2167-0870.1000178

Cited by 16 publications

(18 citation statements)

References 41 publications

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“…The study protocol, randomisation, inclusion and exclusion criteria, and study products in the clinical studies that intervened with a whole grain-rich diet ( https://clinicaltrials.gov , ID-no: NCT01731366) or a low-gluten diet ( https://clinicaltrials.gov , ID-no: NCT01719913) are described previously 22 . Both studies were performed in accordance with relevant regulations and written informed consent was obtained from all participants.…”

Section: Methodsmentioning

confidence: 99%

“…We previously investigated the impact of 8 weeks dietary interventions on human metabolic health outcomes in two Danish randomized cross-over trials with a whole grain-rich diet or low-gluten diet, associated with a beneficial and non-beneficial impact on metabolic health, respectively 22 – 24 , and identified weight loss as a response to each of the interventions relative to a refined grain diet 25 , 26 . It has however been argued that no single dietary strategy would be appropriate for all individuals and that certain biomarkers can be important in relation to predisposition for weight loss 7 .…”

Section: Introductionmentioning

confidence: 99%

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Data integration for prediction of weight loss in randomized controlled dietary trials

Nielsen

Helenius

García

et al. 2020

Sci Rep

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Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84–0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data integration for prediction of weight loss in randomized controlled dietary trials

Nielsen

Helenius

García

et al. 2020

Sci Rep

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“…Importantly, participants had to meet similar inclusion and exclusion criteria in both trials and the different modes of interventions in the two studies are irrelevant for the present study, which exclusively was based on analyses of data obtained at baseline prior to start of the specific interventions [15]. The trials were registered at www.clinicaltrials.gov (NCT01719913 and NCT01731366) and were conducted in accordance with the Helsinki declaration II and endorsed by the Danish Data Protection Agency (2007-54-0269) and The Ethical Committee of the Capital Region of Denmark approved the trials (H-2-2012-065).…”

Section: Methodsmentioning

confidence: 99%

The intestinal microbiome is a co-determinant of the postprandial plasma glucose response

et al. 2020

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Elevated postprandial plasma glucose is a risk factor for development of type 2 diabetes and cardiovascular disease. We hypothesized that the inter-individual postprandial plasma glucose response varies partly depending on the intestinal microbiome composition and function. We analyzed data from Danish adults (n = 106), who were self-reported healthy and attended the baseline visit of two previously reported randomized controlled cross-over trials within the Gut, Grain and Greens project. Plasma glucose concentrations at five time points were measured before and during three hours after a standardized breakfast. Based on these data, we devised machine learning algorithms integrating bio-clinical, as well as shotgun-sequencing-derived taxa and functional potentials of the intestinal microbiome to predict individual postprandial glucose excursions. In this post hoc study, we found microbial and clinical features, which predicted up to 48% of the inter-individual variance of postprandial plasma glucose responses (Pearson correlation coefficient of measured vs. predicted values, R = 0.69, 95% CI: 0.45 to 0.84, p<0.001). The features were age, fasting serum triglycerides, systolic blood pressure, BMI, fasting total serum cholesterol, abundance of Bifidobacterium genus, richness of metagenomics species and abundance of a metagenomic species annotated to Clostridiales at order level. A model based only on microbial features predicted up to 14% of the variance in postprandial plasma glucose excursions (R = 0.37, 95% CI: 0.02 to 0.64, p = 0.04). Adding fasting glycaemic measures to the model including microbial and bio-clinical features increased the predictive power to R = 0.78 (95% CI: 0.59 to 0.89, p<0.001), explaining more than 60% of the inter-individual variance of postprandial plasma glucose concentrations. The outcome of the study points to a potential role of the taxa and functional potentials of the intestinal microbiome. If validated in larger studies our findings may be included in future algorithms attempting to develop personalized nutrition, especially for prediction of individual blood glucose excursions in dys-glycaemic individuals.

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“…This study included cross-sectional baseline data from six human intervention trials: 3G, OPUS, DIOGENES, RIGHT, MyNewGut (MNG), and PROKA (10–15). The studies were registered at http://www.clinicaltrials.gov; 3G (NCT01719913 and NCT01731366); SHOPUS (NCT01195610); DIOGENES (NCT00390637); RIGHT (NCT02358122); MNG: (NCT02215343); PROKA (NCT01561131) and approved by the Research Ethics Committees of the Capital Region of Denmark in accordance with Helsinki Declaration 3G (H-2-2012-064 and H-2-2012-065); SHOPUS (H-3-2010-058); RIGHT (H-1-2014-062); MNG (H-4-2014-052); and PROKA (H-2-2011-145) or the local ethical committees in the respective countries (16).…”

Section: Methodsmentioning

confidence: 99%

Sagittal abdominal diameter and waist circumference are equally good as identifiers of cardiometabolic risk

Møller

Ritz

Vuholm

et al. 2019

Preprint

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BackgroundBody mass index (BMI) and waist circumference (WC) are commonly used markers of cardiometabolic risk. However, sagittal abdominal diameter (SAD) has been proposed to be a better marker of intra-abdominal obesity compared to WC and might better associate with metabolic disturbances in high-risk populations. The objective of this study was to compare SAD, WC, and BMI as determinants of an adverse metabolic phenotype.MethodAnthropometric and metabolic measures of 1516 overweight or obese individuals with features of the metabolic syndrome were included to examine differences between SAD, WC and BMI as measures of an adverse metabolic phenotype. Multiple linear regression and logistic regression models were used to investigate the association between SAD, WC, and BMI and markers of metabolic syndrome, insulin resistance, blood lipids, and low grade inflammation.ResultsBoth SAD and WC correlated with BMI, but as BMI increased, SAD proportionately estimated higher abdominal adiposity compared to WC (slope = 0.0037 (0.0029; 0.0046), p<0.0001). We did not find major differences between SAD, WC and BMI in explained variance in models with the different markers of metabolic risk. Furthermore, we did not find differences between SAD and WC in the ability to identify individuals with metabolic syndrome according to the International Diabetes Federation (IDF) cut-offs, but a few differences from BMI were indicated but mostly before adjustments. Moreover, the differences between SAD and WC associations were not modified by sex or degree of adiposity, but identification of individuals with a metabolic phenotype was generally better in women.ConclusionThese data indicate that SAD and WC are equally good indicators of an adverse metabolic phenotype. Thus, from a public health perspective choice of anthropometric measure may depend only on what is the most practical method in a given situation.

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Two Randomized Cross-Over Trials Assessing the Impact of Dietary Gluten or Wholegrain on the Gut Microbiome and Host Metabolic Health

Cited by 16 publications

References 41 publications

Data integration for prediction of weight loss in randomized controlled dietary trials

Data integration for prediction of weight loss in randomized controlled dietary trials

The intestinal microbiome is a co-determinant of the postprandial plasma glucose response

Sagittal abdominal diameter and waist circumference are equally good as identifiers of cardiometabolic risk

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