Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study

Abstract Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy in the general population. We used data f...

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Autores principales:	Guy Fagherazzi, Lu Zhang, Gloria Aguayo, Jessica Pastore, Catherine Goetzinger, Aurélie Fischer, Laurent Malisoux, Hanen Samouda, Torsten Bohn, Maria Ruiz-Castell, Laetitia Huiart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fada0620fe1a4356a0fc65174288dcb6
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spelling	oai:doaj.org-article:fada0620fe1a4356a0fc65174288dcb62021-12-02T14:53:48ZTowards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study10.1038/s41598-021-95487-52045-2322https://doaj.org/article/fada0620fe1a4356a0fc65174288dcb62021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95487-5https://doaj.org/toc/2045-2322Abstract Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy in the general population. We used data from ORISCAV-LUX 2, a nationwide, cross-sectional, population-based study. On the 1356 participants, we used a machine learning semi-supervised cluster method guided by body mass index (BMI) and glycated hemoglobin (HbA1c), and a set of 29 cardiometabolic variables, to identify subgroups of interest for cardiometabolic health. Cluster stability was assessed with the Jaccard similarity index. We have observed 4 clusters with a very high stability (ranging between 92 and 100%). Based on distinctive features that deviate from the overall population distribution, we have labeled Cluster 1 (N = 729, 53.76%) as “Healthy”, Cluster 2 (N = 508, 37.46%) as “Family history—Overweight—High Cholesterol “, Cluster 3 (N = 91, 6.71%) as “Severe Obesity—Prediabetes—Inflammation” and Cluster 4 (N = 28, 2.06%) as “Diabetes—Hypertension—Poor CV Health”. Our work provides an in-depth characterization and thus, a better understanding of cardiometabolic health in the general population. Our data suggest that such a clustering approach could now be used to define more targeted and tailored strategies for the prevention of cardiometabolic diseases at a population level. This study provides a first step towards precision cardiometabolic prevention and should be externally validated in other contexts.Guy FagherazziLu ZhangGloria AguayoJessica PastoreCatherine GoetzingerAurélie FischerLaurent MalisouxHanen SamoudaTorsten BohnMaria Ruiz-CastellLaetitia HuiartNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guy Fagherazzi Lu Zhang Gloria Aguayo Jessica Pastore Catherine Goetzinger Aurélie Fischer Laurent Malisoux Hanen Samouda Torsten Bohn Maria Ruiz-Castell Laetitia Huiart Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
description	Abstract Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy in the general population. We used data from ORISCAV-LUX 2, a nationwide, cross-sectional, population-based study. On the 1356 participants, we used a machine learning semi-supervised cluster method guided by body mass index (BMI) and glycated hemoglobin (HbA1c), and a set of 29 cardiometabolic variables, to identify subgroups of interest for cardiometabolic health. Cluster stability was assessed with the Jaccard similarity index. We have observed 4 clusters with a very high stability (ranging between 92 and 100%). Based on distinctive features that deviate from the overall population distribution, we have labeled Cluster 1 (N = 729, 53.76%) as “Healthy”, Cluster 2 (N = 508, 37.46%) as “Family history—Overweight—High Cholesterol “, Cluster 3 (N = 91, 6.71%) as “Severe Obesity—Prediabetes—Inflammation” and Cluster 4 (N = 28, 2.06%) as “Diabetes—Hypertension—Poor CV Health”. Our work provides an in-depth characterization and thus, a better understanding of cardiometabolic health in the general population. Our data suggest that such a clustering approach could now be used to define more targeted and tailored strategies for the prevention of cardiometabolic diseases at a population level. This study provides a first step towards precision cardiometabolic prevention and should be externally validated in other contexts.
format	article
author	Guy Fagherazzi Lu Zhang Gloria Aguayo Jessica Pastore Catherine Goetzinger Aurélie Fischer Laurent Malisoux Hanen Samouda Torsten Bohn Maria Ruiz-Castell Laetitia Huiart
author_facet	Guy Fagherazzi Lu Zhang Gloria Aguayo Jessica Pastore Catherine Goetzinger Aurélie Fischer Laurent Malisoux Hanen Samouda Torsten Bohn Maria Ruiz-Castell Laetitia Huiart
author_sort	Guy Fagherazzi
title	Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
title_short	Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
title_full	Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
title_fullStr	Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
title_full_unstemmed	Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
title_sort	towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based oriscav-lux 2 study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fada0620fe1a4356a0fc65174288dcb6
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Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study

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