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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-95487-5 |
| _version_ | 1818432584162476032 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T16:07:31Z |
| format | Article |
| id | doaj.art-fada0620fe1a4356a0fc65174288dcb6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T16:07:31Z |
| publishDate | 2021-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fada0620fe1a4356a0fc65174288dcb62022-12-21T22:55:03ZengNature PortfolioScientific Reports2045-23222021-08-0111111110.1038/s41598-021-95487-5Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 studyGuy Fagherazzi0Lu Zhang1Gloria Aguayo2Jessica Pastore3Catherine Goetzinger4Aurélie Fischer5Laurent Malisoux6Hanen Samouda7Torsten Bohn8Maria Ruiz-Castell9Laetitia Huiart10Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthQuantitative Biology Unit, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of HealthAbstract 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.https://doi.org/10.1038/s41598-021-95487-5 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-021-95487-5 |
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