Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population‐Based KORA Study. Issue 16 (31st July 2018)

• Record Type: Journal Article
• Authors: Riedl, Anna; Wawro, Nina; Gieger, Christian; Meisinger, Christa; Peters, Annette; Roden, Michael; Kronenberg, Florian; Herder, Christian; Rathmann, Wolfgang; Völzke, Henry; Reincke, Martin; Koenig, Wolfgang; Wallaschofski, Henri; Hauner, Hans; Daniel, Hannelore; Linseisen, Jakob
• Is Part Of: Molecular nutrition & food research. Volume 62:Issue 16(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-31
• Subjects: cardiometabolic disease -- cluster analysis -- enable‐Cluster -- metabolic phenotype -- metabotype; Food -- Biotechnology -- Periodicals; Food -- Microbiology -- Periodicals; Nutrition -- Periodicals; Food -- Toxicology -- Periodicals; Nutrition -- Periodicals; Food Microbiology -- Periodicals; Food Technology -- Periodicals; Molecular Biology -- Periodicals; 664.0705
• DOI: 10.1002/mnfr.201800117
• Languages: English
• ISSNs: 1613-4125

Abstract

Abstract : Scope: "Metabotyping" describes the grouping of metabolically similar individuals. We aimed to identify valid metabotypes in a large cohort for targeted dietary intervention, for example, for disease prevention. Methods and results: We grouped 1729 adults aged 32–77 years of the German population‐based KORA F4 study (2006–2008) using k‐means cluster analysis based on 34 biochemical and anthropometric parameters. We identified three metabolically distinct clusters showing significantly different biochemical parameter concentrations. Cardiometabolic disease status was determined at baseline in the F4 study and at the 7 year follow‐up termed FF4 (2013/2014) to compare disease prevalence and incidence between clusters. Cluster 3 showed the most unfavorable marker profile with the highest prevalence of cardiometabolic diseases. Also, disease incidence was higher in cluster 3 compared to clusters 2 and 1, respectively, for hypertension (41.2%/25.3%/18.2%), type 2 diabetes (28.3%/5.1%/2.0%), hyperuricemia/gout (10.8%/2.3%/0.7%), dyslipidemia (19.2%/18.3%/5.6%), all metabolic (54.5%/36.8%/19.7%), and all cardiovascular (6.3%/5.5%/2.3%) diseases together. Conclusion: Cluster analysis based on an extensive set of biochemical and anthropometric parameters allows the identification of comprehensive metabotypes that were distinctly different in cardiometabolic disease occurrence. As a next step, targeted dietary strategies should be developed with the goal of preventing diseases, especially in cluster 3. Abstract : Metabotyping describes the grouping of metabolically similar individuals. The aim of this study is to identify valid and comprehensive metabotypes in the German population‐based KORA F4 study. Three metabolically distinct clusters are identified. These grouped clearly with the occurrence of cardiometabolic diseases. The metabotypes may be used for targeted dietary prevention and therapy of cardiometabolic diseases.