TY - JOUR
T1 - Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population-Based KORA Study
AU - Riedl, Anna
AU - Wawro, Nina
AU - Gieger, Christian
AU - Meisinger, Christa
AU - Peters, Annette
AU - Roden, Michael
AU - Kronenberg, Florian
AU - Herder, Christian
AU - Rathmann, Wolfgang
AU - Völzke, Henry
AU - Reincke, Martin
AU - Koenig, Wolfgang
AU - Wallaschofski, Henri
AU - Hauner, Hans
AU - Daniel, Hannelore
AU - Linseisen, Jakob
N1 - Publisher Copyright:
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - cardiometabolic disease
KW - cluster analysis
KW - enable-Cluster
KW - metabolic phenotype
KW - metabotype
UR - http://www.scopus.com/inward/record.url?scp=85052097156&partnerID=8YFLogxK
U2 - 10.1002/mnfr.201800117
DO - 10.1002/mnfr.201800117
M3 - Article
C2 - 29939495
AN - SCOPUS:85052097156
SN - 1613-4125
VL - 62
JO - Molecular Nutrition and Food Research
JF - Molecular Nutrition and Food Research
IS - 16
M1 - 1800117
ER -