Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease. (February 2020) - Ohukainen, Pauli; Kuusisto, Sanna; Kettunen, Johannes; Perola, Markus; Järvelin, Marjo-Riitta; Mäkinen, Ville-Petteri; Ala-Korpela, Mika
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. Highlights: Data-driven subgrouping algorithm was trained by multivariate lipoprotein data. Four coherent subgroups were identified in two large-scale population-based cohorts. Subgroups had characteristic lipoprotein profiles and risk for CHD. Apolipoprotein B quartiles stratified CHD risk better than multivariate subgroups. Caution on multivariate data-driven subgrouping in risk assessment is warranted.