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

Guy Fagherazzi*, Lu Zhang, Gloria Aguayo, Jessica Pastore, Catherine Goetzinger, Aurélie Fischer, Laurent Malisoux, Hanen Samouda, Torsten Bohn, Maria Ruiz-Castell, Laetitia Huiart

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number16056
Pages (from-to)16056
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - 6 Aug 2021

Keywords

  • Adult
  • Aged
  • Aged, 80 and over
  • Body Mass Index
  • Cardiovascular Diseases/diagnosis
  • Cross-Sectional Studies
  • Female
  • Humans
  • Luxembourg/epidemiology
  • Machine Learning
  • Male
  • Metabolic Diseases/diagnosis
  • Middle Aged
  • Obesity
  • Overweight
  • Risk Factors
  • Supervised Machine Learning

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