Identification of Robust Metabotypes Associated With Increased Cardiometabolic Disease Risk- an Approach for Improved Prevention Through Precision Nutrition

M. Palmnäs-Bédard, V. Skantze, A. Rastgaard-Hansen, J. Dicksved, J. Halkjaer, A. Tjønneland, C. Brunius, R. Landberg. Current Developments in Nutrition 5(Supplement_2):604-604, 7 June 2021.

Abstract

Objectives

We hypothesize that individuals can be grouped into robust metabolic phenotypes (metabotypes) based on biochemical, anthropometric, gut microbial and metabolomics data and that such metabotypes will reflect differences in cardiometabolic disease risk and can act as targets for tailored nutritional prevention. We furthermore hypothesize that diet-gut microbiota interactions will be major determinants of the metabotypes.

Methods

Metabotyping is currently performed based on baseline data from 628 Danish adults from a validation sub-study of the Danish Diet Cancer and Health-Next Generation cohort. Participants were followed for one year, also providing data at 6 and 12 months. Dietary data was obtained by food frequency questionnaire and repeated 24h recalls and data on physical activity, smoking, sociodemographic factors, disease prevalence and use of medication was collected by questionnaires. Untargeted metabolomics and gut microbiota are currently determined. Metabotypes will be identified using clustering algorithms, variable optimization and data integration of the plasma metabolome, the 16S rRNA microbiota and 14 biochemical and anthropometric variables. Differences in habitual diet and physical activity across metabotypes will be determined as well as main metabotype determinants and potential plasma metabolite biomarkers. We will also assess the reproducibility of the metabotypes and biomarkers over time.

Results

Two clusters of individuals were identified by using the currently available clinical and anthropometric data. One of the clusters presented with mean values consistent with overweight, hypertension and dyslipidemia. Next, we will integrate the plasma metabolomics and gut microbial data into the analysis to determine the metabotypes.

Conclusions

We have identified one higher risk group and one lower risk group for cardiometabolic disease and will identify and characterize metabotypes based on more extensive data. Future research will assess whether individuals belonging to different metabotypes respond differently to dietary interventions.




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