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Identification of comprehensive metabotypes associated with cardiometabolic diseases in the population-based KORA study.

Mol. Nutr. Food Res. 62:1800117 (2018)
DOI
Postprint online available 09/2019 Open Access Green as soon as is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Cardiometabolic Disease ; Cluster Analysis ; Enable-cluster ; Metabolic Phenotype ; Metabotype; Metabolic Risk-factors; Personalized Nutrition; Cluster-analysis; Diabetes-mellitus; Women; Health; Determinants; Phenotypes; Patterns; Medicine
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