Pauli Ohukainen, Sanna Kuusisto, Johannes Kettunen, Markus Perola, Marjo-Riitta Järvelin, Ville-Petteri Mäkinen, Mika Ala-Korpela, Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease, Atherosclerosis,Volume 294, 2020, Pages 10-15, ISSN 0021-9150, https://doi.org/10.1016/j.atherosclerosis.2019.12.009
Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease
|Author:||Ohukainen, Pauli1,2,3; Kuusisto, Sanna1,2,3,4; Kettunen, Johannes1,2,3,5;|
1Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
2Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
3Biocenter Oulu, University of Oulu, Oulu, Finland
4NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
5National Institute for Health and Welfare, Helsinki, Finland
6Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland
7Estonian Genome Center, University of Tartu, Tartu, Estonia
8Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
9Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
10Department of Life Sciences, College of Health and Life Sciences, Brunel University London, UK
11Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Australia
12Hopwood Centre for Neurobiology, Lifelong Health Theme, SAHMRI, Australia
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042722683
|Publish Date:|| 2020-04-27
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.
|Pages:||10 - 15|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3121 General medicine, internal medicine and other clinical medicine
PO is supported by the Emil Aaltonen Foundation. JK and MAK are supported by a research grant from the Sigrid Juselius Foundation, Finland. The cohorts and this work have also been supported by funding from the Academy of Finland, Novo Nordisk Foundation and EU.
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).