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 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.

Analysing breast cancer survivors’ acceptance profiles for using an electronic pillbox connected to a smartphone application using Seintinelles, a French community-based research tool

“…Characterising patient groups will allow defining new strategies for individual patients benefiting their needs to optimise health outcomes. Recent research, using profiling principles, found that healthcare for patients with cardiometabolic disease could benefit from more targeted and tailored strategies for the prevention of cardiometabolic diseases at a population level ( Fagherazzi et al, 2021 ). Eventually, post-acute treatment for BC survivors using a medication adherence enhancing eHealth technology can move from a “one-size-fits-all” vision to a tailored follow-up strategy, personalizing care to each BC survivor.…”

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

“…For example, individuals who have obesity, but do not demonstrate any comorbidities, have been subtyped as having "metabolically healthy obesity" [77]. Increasingly, more advanced methods-such as principal component analyses and machine learning based on anthropometric and clinical features-are being used for more refined subclassifications [78][79][80].…”

Identifying obesity subtypes: A review of studies utilising clinical biomarkers and genetic data

“…Fagherazzi et al 18 used various clinical, anthropometric, and laboratory factors to predict BMI and HbA 1c (a measure of average blood sugar levels over the past few months), and then applied a clustering approach to group individuals based on these predictions. The resulting clusters revealed important insights.…”