TY - JOUR
T1 - Towards precision cardiometabolic prevention
T2 - results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study
AU - Fagherazzi, Guy
AU - Zhang, Lu
AU - Aguayo, Gloria
AU - Pastore, Jessica
AU - Goetzinger, Catherine
AU - Fischer, Aurélie
AU - Malisoux, Laurent
AU - Samouda, Hanen
AU - Bohn, Torsten
AU - Ruiz-Castell, Maria
AU - Huiart, Laetitia
N1 - Funding Information:
The ORISCAV-LUX 2 study has been funded by the Luxembourg Institute of Health. No role to be declared of the funding body in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. GF has received consulting fees from Lilly, MSD, Roche Diabetes Care, AstraZeneca, Diabeloop and Danone Research.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/8/6
Y1 - 2021/8/6
N2 - 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.
AB - 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.
KW - Adult
KW - Aged
KW - Aged, 80 and over
KW - Body Mass Index
KW - Cardiovascular Diseases/diagnosis
KW - Cross-Sectional Studies
KW - Female
KW - Humans
KW - Luxembourg/epidemiology
KW - Machine Learning
KW - Male
KW - Metabolic Diseases/diagnosis
KW - Middle Aged
KW - Obesity
KW - Overweight
KW - Risk Factors
KW - Supervised Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85112626232&partnerID=8YFLogxK
UR - https://www.ncbi.nlm.nih.gov/pubmed/34362963
U2 - 10.1038/s41598-021-95487-5
DO - 10.1038/s41598-021-95487-5
M3 - Article
C2 - 34362963
AN - SCOPUS:85112626232
SN - 2045-2322
VL - 11
SP - 16056
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 16056
ER -