Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study

The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters...

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Main Authors: Chetana Dahal, Nina Wawro, Christa Meisinger, Taylor A. Breuninger, Barbara Thorand, Wolfgang Rathmann, Wolfgang Koenig, Hans Hauner, Annette Peters, Jakob Linseisen
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Life
Subjects:
Online Access:https://www.mdpi.com/2075-1729/12/10/1460
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author Chetana Dahal
Nina Wawro
Christa Meisinger
Taylor A. Breuninger
Barbara Thorand
Wolfgang Rathmann
Wolfgang Koenig
Hans Hauner
Annette Peters
Jakob Linseisen
author_facet Chetana Dahal
Nina Wawro
Christa Meisinger
Taylor A. Breuninger
Barbara Thorand
Wolfgang Rathmann
Wolfgang Koenig
Hans Hauner
Annette Peters
Jakob Linseisen
author_sort Chetana Dahal
collection DOAJ
description The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.
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spelling doaj.art-24d06f6593b649e39acc309766679b182023-11-24T00:55:15ZengMDPI AGLife2075-17292022-09-011210146010.3390/life12101460Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA StudyChetana Dahal0Nina Wawro1Christa Meisinger2Taylor A. Breuninger3Barbara Thorand4Wolfgang Rathmann5Wolfgang Koenig6Hans Hauner7Annette Peters8Jakob Linseisen9Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyIndependent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyIndependent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyIndependent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyGerman Center for Diabetes Research, Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyGerman Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336 Munich, GermanyElse Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, GermanyInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyIndependent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, GermanyThe aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.https://www.mdpi.com/2075-1729/12/10/1460metabotypecluster analysisparameter selectionclinical markermetabolic diseasescardiovascular diseases
spellingShingle Chetana Dahal
Nina Wawro
Christa Meisinger
Taylor A. Breuninger
Barbara Thorand
Wolfgang Rathmann
Wolfgang Koenig
Hans Hauner
Annette Peters
Jakob Linseisen
Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study
Life
metabotype
cluster analysis
parameter selection
clinical marker
metabolic diseases
cardiovascular diseases
title Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study
title_full Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study
title_fullStr Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study
title_full_unstemmed Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study
title_short Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study
title_sort optimized metabotype definition based on a limited number of standard clinical parameters in the population based kora study
topic metabotype
cluster analysis
parameter selection
clinical marker
metabolic diseases
cardiovascular diseases
url https://www.mdpi.com/2075-1729/12/10/1460
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