Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology: disclosure of fundamental structural and metabolic relationships

Abstract

Abstract Background Quantitative lipoprotein analytics using nuclear magnetic resonance (NMR) spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilized also in large biobanks. It allows the comprehensive characterization of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualized here in relation to lipoprotein metabolism with particular attention on the fundamental characteristics of subclass particle numbers, lipid concentrations and compositional measures. Methods and Results The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohorts 1966 and 1986 with 5651 and 5605 participants, respectively. All results were highly consistent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveals that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations. Conclusions The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is important for the precise interpretation of novel therapeutic opportunities and for fully utilizing the potential of forthcoming analyses of genetic and metabolic data in large biobanks.