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Riedl, A. ; Wawro, N. ; Gieger, C. ; Meisinger, C. ; Peters, A. ; Roden, M.* ; Kronenberg, F.* ; Herder, C.* ; Rathmann, W.* ; Völzke, H.* ; Reincke, M.* ; Koenig, W.* ; Wallaschofski, H.* ; Hauner, H.* ; Daniel, H.* ; Linseisen, J.

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

Mol. Nutr. Food Res. 62:1800117 (2018)
Postprint DOI PMC
Open Access Green
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cardiometabolic Disease ; Cluster Analysis ; Enable-cluster ; Metabolic Phenotype ; Metabotype; Metabolic Risk-factors; Personalized Nutrition; Cluster-analysis; Diabetes-mellitus; Women; Health; Determinants; Phenotypes; Patterns; Medicine
Sprache
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 1613-4125
e-ISSN 1613-4133
Zeitschrift Molecular Nutrition and Food Research
Quellenangaben Band: 62, Heft: 16, Seiten: , Artikelnummer: 1800117 Supplement: ,
Verlag Wiley
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Begutachtungsstatus Peer reviewed
Institut(e) Independent Research Group Clinical Epidemiology (KEPI)
Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
30504 - Mechanisms of Genetic and Environmental Influences on Health and Disease
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-502900-001
G-504000-002
G-508600-002
G-504091-001
G-504000-010
G-504090-001
DOI 10.1002/mnfr.201800117
WOS ID WOS:000442592900006
Scopus ID 85052097156
PubMed ID 29939495
Erfassungsdatum 2018-09-10
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