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

被引:21
作者
Riedl, Anna [1 ,2 ,3 ,4 ]
Wawro, Nina [1 ,2 ,3 ,4 ]
Gieger, Christian [2 ,3 ,5 ]
Meisinger, Christa [1 ,2 ,4 ]
Peters, Annette [2 ,3 ]
Roden, Michael [3 ,6 ,7 ]
Kronenberg, Florian [8 ]
Herder, Christian [3 ,7 ]
Rathmann, Wolfgang [3 ,9 ]
Voelzke, Henry [3 ,10 ,11 ]
Reincke, Martin [12 ]
Koenig, Wolfgang [10 ,13 ,14 ]
Wallaschofski, Henri [15 ]
Hauner, Hans [16 ,17 ,18 ]
Daniel, Hannelore [19 ]
Linseisen, Jakob [1 ,2 ,4 ,17 ]
机构
[1] German Res Ctr Environm Hlth, Helmholtz Zentrum Munchen, Independent Res Grp Clin Epidemiol, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany
[2] German Res Ctr Environm Hlth, Helmholtz Zentrum Munchen, Inst Epidemiol 2, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany
[3] German Ctr Diabet Res, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany
[4] Ludwig Maximilians Univ Munchen, Klinikum Augsburg, Univ Zentrum Gesundheitswissensch, Chair Epidemiol,UNIKA T, Neusasser Str 47, D-86156 Augsburg, Germany
[5] German Res Ctr Environm Hlth GmbH, Helmholtz Zentrum Munchen, Res Unit Mol Epidemiol, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany
[6] Heinrich Heine Univ Dusseldorf, Med Fac, Div Endocrinol & Diabetol, Hennekamp 65, D-40225 Dusseldorf, Germany
[7] Heinrich Heine Univ Dusseldorf, Leibniz Ctr Diabet Res, German Diabet Ctr, Inst Clin Diabetol, Hennekamp 65, D-40225 Dusseldorf, Germany
[8] Med Univ Innsbruck, Dept Med Genet Mol & Clin Pharmacol, Div Genet Epidemiol, Schopfstr 41, A-6020 Innsbruck, Austria
[9] Heinrich Heine Univ Dusseldorf, Leibniz Ctr Diabet Res, German Diabet Ctr, Inst Biometr & Epidemiol, Aufm Hennekamp 65, D-40225 Dusseldorf, Germany
[10] DZHK German Ctr Cardiovasc Res, Partner Site Munich Heart Alliance, Pettenkoferstr 8a & 9, D-80336 Munich, Germany
[11] Univ Med Greifswald, Inst Community Med, Walther Rathenau Str 48, D-17475 Greifswald, Germany
[12] Ludwig Maximilians Univ Munchen, Klinikum Univ Munchen, Med Klin & Poliklin 4, Ziemssenstr 1, D-81377 Munich, Germany
[13] Tech Univ Munich, Deutsch Herzzentrum Munchen, Lazarettstr 36, D-80636 Munich, Germany
[14] Univ Ulm, Med Ctr, Dept Internal Med Cardiol 2, Albert Einstein Allee 23, D-89081 Ulm, Germany
[15] Univ Med Greifswald, Inst Clin Chem & Lab Med, Ferdinand Sauerbruch Str, D-17489 Greifswald, Germany
[16] Tech Univ Munich, Else Kroner Fresenius Ctr Nutr Med, Gregor Mendel Str 2, D-85354 Freising Weihenstephan, Germany
[17] Tech Univ Munich, ZIEL Inst Food & Hlth, Weihenstephaner Berg 1, D-85354 Freising Weihenstephan, Germany
[18] Tech Univ Munich, Klinikum Rechts Isar, Inst Nutr Med, Uptown Munchen Campus D,Georg Brauchle Ring 60-62, D-80992 Munich, Germany
[19] Tech Univ Munich, Gregor Mendel Str 2, D-85354 Freising Weihenstephan, Germany
关键词
cardiometabolic disease; cluster analysis; enable-Cluster; metabolic phenotype; metabotype; METABOLIC RISK-FACTORS; PERSONALIZED NUTRITION; CLUSTER-ANALYSIS; DIABETES-MELLITUS; WOMEN; HEALTH; DETERMINANTS; PHENOTYPES; PATTERNS; MEDICINE;
D O I
10.1002/mnfr.201800117
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
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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页数:9
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