Differential effects of Nordic and Vegetarian diets on lipid metabolism, gut microbiome and cardiometabolic risk factors: A multi-omic perspective from a randomized clinical intervention trial
Huber, H.; Schieren, A.; Donkers, A.; Mantri, A.; Seel, W.; Stoffel-Wagner, B.; Coenen, M.; Weinhold, L.; Schmid, M.; Krawitz, P.; Hartmann, B.; Holst, J. J.; Leidner, J.; Pecht, T.; Bonaguro, L.; Yaghmour, M.; Thiele, C.; Noethen, M. M.; Stehle, P.; Simon, M.-C.
Show abstract
BackgroundBeneficial effects of diets with predominance of plant-based foods as fruits, vegetables, whole grain and plant-protein and less animal-based foods, or so-called "plant-based" diets, on cardiometabolic risk have been reported. We aimed to examine the effects of two distinct plant-based diets on intermediate cardiometabolic risk factors, particularly lipid metabolism, while also considering the impact of the gut microbiome, genetic predisposition, and immune status on the metabolic response to a dietary change. MethodsIn this randomized, controlled dietary intervention trial, 120 obese adults (59 {+/-} 1 years, 70 females) consumed an isoenergetic Nordic (ND) or a lacto-ovo vegetarian diet (VD) or maintained their habitual diet (control group) for six weeks. At baseline and after the end of the trial, in-depth metabolic characterization was conducted, including measurement of incretins such as glucagon-like peptide 1 (GLP-1), postprandial lipids with lipidomic profiling, and microbiome analysis. Genetic makeup and peripheral immune system composition were characterized at baseline. ResultsND intervention beneficially altered lipid metabolism up to 15%. The largest changes were observed in participants with high genetic predisposition for hyperlipidemia, while lipid metabolism remained stable upon VD. The changes observed were associated with specific microbial signatures and pathways. GLP-1 levels remained stable during the study period. ConclusionThe metabolic response to a dietary change in obese adults is linked to the individual genetic risk, baseline microbiome composition, and immune phenotype, pointing towards a personalized nutritional approach in preventing cardiometabolic diseases.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The effects of time-restricted eating vs. standard dietary advice on weight, metabolic health and the consumption of processed food: A pragmatic randomised controlled trial in community-based adults 94%
- Almond Consumption Improves Inflammatory Profiles Independent of Weight Change: A 6-Week Randomized Controlled Trial in Adults with Obesity 94%
- Study of the Effect of Intestinal Microbes on Obesity: A Bibliometric Analysis 93%
Similar papers in this journal
- Erythrocyte n-6 polyunsaturated fatty acids, gut microbiota and incident type 2 diabetes: a prospective cohort study 95%
- Application of n-of-1 clinical trials in personalized nutrition research: a trial protocol for Westlake N-of-1 Trials for Macronutrient Intake (WE-MACNUTR) 93%
- Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes 92%
Similar papers in this journal
- Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes 95%
- Oral supplementation of gut microbial metabolite indole-3-acetate alleviates diet-induced steatosis and inflammation in mice 94%
- Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins 94%
Similar papers in this journal
- Lysates of Methylococcus capsulatus Bath induce a lean-like microbiota, intestinal FoxP3+RORγt+IL-17+ Tregs and improve metabolism 95%
- Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption 94%
- Fasting alters the gut microbiome with sustained blood pressure and body weight reduction in metabolic syndrome patients 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.