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...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-09-01
|
Series: | Life |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-1729/12/10/1460 |
_version_ | 1797471981377945600 |
---|---|
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. |
first_indexed | 2024-03-09T19:56:44Z |
format | Article |
id | doaj.art-24d06f6593b649e39acc309766679b18 |
institution | Directory Open Access Journal |
issn | 2075-1729 |
language | English |
last_indexed | 2024-03-09T19:56:44Z |
publishDate | 2022-09-01 |
publisher | MDPI AG |
record_format | Article |
series | Life |
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 |
work_keys_str_mv | AT chetanadahal optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT ninawawro optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT christameisinger optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT taylorabreuninger optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT barbarathorand optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT wolfgangrathmann optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT wolfgangkoenig optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT hanshauner optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT annettepeters optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy AT jakoblinseisen optimizedmetabotypedefinitionbasedonalimitednumberofstandardclinicalparametersinthepopulationbasedkorastudy |