Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population-Based KORA Study

Anna Riedl, Nina Wawro, Christian Gieger, Christa Meisinger, Annette Peters, Michael Roden, Florian Kronenberg, Christian Herder, Wolfgang Rathmann, Henry Völzke, Martin Reincke, Wolfgang Koenig, Henri Wallaschofski, Hans Hauner, Hannelore Daniel, Jakob Linseisen

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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.

Original languageEnglish
Article number1800117
JournalMolecular Nutrition and Food Research
Volume62
Issue number16
DOIs
StatePublished - Aug 2018

Keywords

  • cardiometabolic disease
  • cluster analysis
  • enable-Cluster
  • metabolic phenotype
  • metabotype

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