Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease

Pauli Ohukainen, Sanna Kuusisto, Johannes Kettunen, Markus Perola, Marjo Riitta Järvelin, Ville-Petteri Makinen, Mika Ala-Korpela

Research output: Contribution to journalArticle

Abstract

Background and aims: Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts. Methods: We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles. Results: The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour. Conclusions: These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment.

Original languageEnglish
Pages (from-to)10-15
Number of pages6
JournalAtherosclerosis
Volume294
DOIs
Publication statusPublished - 1 Feb 2020

Keywords

  • Apolipoprotein B
  • Artificial intelligence
  • CHD
  • Data-driven
  • Lipoproteins
  • Population subgroups
  • Risk assessment

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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